Miracle-dz/UniFAR-FTUs huggingface dataset:
The resting potential of nerve cells and the Na,K-pump

In this pedagogical paper a coherent explanation of the resting potential of nerve cells is given in terms of its determining factors. These are the currents of active transport of the ions to which the membrane is permeable, their membrane permeabilities and their concentrations in the extracellular fluid. They play the role of the independent variables in the problem and simultaneously also determine the concentrations of the permeating ions inside the cell. The resting state is assumed to be a stationary state, and the resting potential is understood to be the membrane potential in this state. The explanation presented here differs from the conventional one found in textbooks with respect to the handling of the intracellular concentrations of the permeant ions. In the conventional description the causal relations between these and the independent variables are not taken into account, and the intracellular concentrations are treated like additional independent variables. The ”causal” theory presented here leads to a simple formula for the resting potential in terms of the currents of active transport (pumping by the Na,K-pump and carrier transport) and the membrane permeabilities for sodium and potassium ions. In addition, it leads to two results not obtained in the conventional treatment: An upper bound exists for the magnitude of the resting potential, and the difference between the resting potential and the diffusion potential, which can be measured after poisoning of the ion pumps, is small.
Microstructural model for cyclic hardening in F-actin networks crosslinked by $\alpha$-actinin

The rheology of F-actin networks has attracted a great attention during the last years. In order to gain a complete understanding of the rheological properties of these novel materials, it is necessary the study in a large deformations regime to alter their internal structure. In this sense, Schmoller et al. 2010 showed that the reconstituted networks of F-actin crosslinked with $\alpha$-actinin unexpectedly harden when they are subjected to a cyclical shear. This observation contradicts the expected Mullins effect observed in most soft materials, such as rubber and living tissues, where a pronounced softening is observed when they are cyclically deformed. We think that the key to understand this stunning effect is the gelation process. To define it, the most relevant constituents are the chemical crosslinks -$\alpha$-actinin-, the physical crosslinks -introduced by the entanglement of the semiflexible network- and the interaction between them. As a consequence of this interaction, a pre-stressed network emerges and introduces a feedback effect, where the pre-stress also regulates the adhesion energy of the $\alpha$-actinin, setting the structure in a metastable reference configuration. Therefore, the external loads and the evolvement of the trapped stress drive the microstructural changes during the cyclic loading protocol. In this work, we propose a micromechanical model into the framework of nonlinear continuum mechanics. The mechanics of the F-actin filaments is modeled using the wormlike chain model for semiflexible filaments and the gelation process is modeled as mesoscale dynamics for the $\alpha$-actinin and physical crosslink.The model has been validated with reported experimental results.
Adaptive Artificial Personalities

[Reeves and Nass 1996]. Users shout at the screen when they are frustrated with the software, while science fiction authors keep envisioning emotional androids and sarcastic AI assistants. Various start-up companies promise to bring artifi­ cial beings with endearing personalities into our homes, such as EmoTech’s Olly1 whose adaptive personality was the most emphasized feature in advertising the product. Socially intelligent assistants, be they disembodied voices or physical robots moving through the shared environment, are expected to become a common sight, especially in the context of care [Janowski et al. 2018]. Equipping such agents with a compelling personality is one important step toward establishing a relationship between human and machine [Breazeal 2004], fostering trust and encouraging long-term use after the novelty effect wears off. Personality in this context means a person’s disposition to respond to certain events in a particular manner [Argyle and Little 1972]. Examples from theater [Laurel 1997] or animated films [op den Akker and Bruijnes 2012] have taught us that a character’s reactions to events and how they line up with their goals are what ensures the engagement of human observers. Laurel [1997] argues that giving an interface human-like dispositions helps humans to predict its behavior. These dis­ positions, in turn, are expressed through and inferred from behavior cues such as interruptions [Rogers and Jones 1975] and gaze behavior [Argyle and Cook 1976]. Adaptive Artificial Personalities
Quantifying Loss in Automated Market Makers

We consider the market microstructure of automated market making and, specifically, constant function market makers (CFMMs), from the economic perspective of passive liquidity providers (LPs). In a frictionless, continuous-time Black-Scholes setting and in the absence of trading fees, we decompose the return of an LP into a instantaneous market risk component and a non-negative, non-decreasing, and predictable component which we call "loss-versus-rebalancing'' (ŁVR, pronounced "lever''). Market risk can be fully hedged, but once eliminated, ŁVR remains as a running cost that must be offset by trading fee income in order for liquidity provision to be profitable. ŁVR is distinct from the more commonly known metric of "impermanent loss'' or "divergence loss''; this latter metric is more fundamentally described as "loss-versus-holding'' and is not a true running cost. We express ŁVR simply and in closed-form: instantaneously, it is the scaled product of the variance of prices and the marginal liquidity available in the pool. As such, ŁVR is easily calibrated to market data and specific CFMM structure. ŁVR provides tradeable insight in both the ex ante and ex post assessment of CFMM LP investment decisions, and can also inform the design of CFMM protocols. For a more complete version of this paper, please refer to https://arxiv.org/pdf/2208.06046. pdf.
The dynamics of the tip vortices shed by a tip-loaded propeller with winglets

Abstract The tip vortices shed by two marine propellers are studied, relying on large-eddy simulation, using a cylindrical grid consisting of 5 billion points. A tip-loaded design, featuring winglets at the tips of its blades, is compared against a conventional one at the design advance coefficient and a model-scale Reynolds number equal to 432 000. The tip-loaded propeller achieves improved performance, but produces also more intense tip vortices. The propeller with winglets actually generates two vortices from the tip of each blade, originating at the edge of each winglet and at the junction between the winglets and the blades. They merge at a short distance downstream, within a diameter from the propeller plane. The helical vortices originating from this merging process experience a slower instability, in comparison with the tip vortices in the wake of the conventional propeller, persisting further downstream, due to the weaker shear with the wakes shed by the following blades. The results of the simulations highlight that splitting the single tip vortex of a conventional propeller into two smaller vortices by means of winglets does not imply necessarily the generation of weaker vortices and lower negative peaks of pressure at their core: the geometry of the winglets needs to be carefully optimized to achieve this target.
Exploring primary healthcare practitioners’ experiences regarding the coronavirus disease 2019 (COVID-19) pandemic in KwaZulu-Natal, South Africa

Abstract Background: The coronavirus disease 2019 (COVID-19) has spread rapidly around the world since the initial outbreak in Wuhan, China. With the emergence of the Omicron variant, South Africa is presently the epicentre of the COVID-19 pandemic in sub-Saharan Africa. Healthcare workers have been at the forefront of the pandemic in terms of screening, early detection and clinical management of suspected and confirmed COVID-19 cases. Since the beginning of the outbreak, little has been reported on how healthcare workers have experienced the COVID-19 pandemic in South Africa, particularly within a low-income, rural primary care context. Methods: The purpose of the present qualitative study design was to explore primary healthcare practitioners’ experiences regarding the COVID-19 pandemic at two selected primary healthcare facilities within a low-income rural context in KwaZulu-Natal, South Africa. Data were collected from a purposive sample of 15 participants, which consisted of nurses, physiotherapists, pharmacists, community caregivers, social workers and clinical associates. The participants were both men and women who were all above the age of 20. Data were collected through individual, in-depth face-to-face interviews using a semi-structured interview guide. Audio recordings were transcribed verbatim. Data were analysed manually by thematic analysis following Tech’s steps of data analysis. Results: Participants reported personal, occupational and community-related experiences related to the COVID-19 pandemic in South Africa. Personal experiences of COVID-19 yielded superordinate themes of psychological distress, self-stigma, disruption of the social norm, Epiphany and conflict of interest. Occupational experiences yielded superordinate themes of staff infections, COVID-19-related courtesy stigma, resource constraints and poor dissemination of information. Community-related experiences were related to struggles with societal issues, clinician-patient relations and COVID-19 mismanagement of patients. Conclusion: The findings of this study suggest that primary healthcare practitioners’ experiences around COVID-19 are attributed to the catastrophic effects of the COVID-19 pandemic with the multitude of psychosocial consequences forming the essence of these experiences. Ensuring availability of reliable sources of information regarding the pandemic as well as psychosocial support could be valuable in helping healthcare workers cope with living and working during the pandemic.
Predicting protein content of silage maize using remotely sensed multispectral imagery and proximal leaf sensing

Abstract Timely estimation of silage maize protein provides an effective decision to adapt optimized strategies for nitrogen fertilizer management and also harvesting time for farmers. So, this research aimed to investigate whether using vegetative indices (VIs) derived from UAV remotely sensed multispectral (with 520–900 nm wavelengths) imagery and also Soil Plant Analysis Development (SPAD) greenness index can be used to detect the leaf protein concentration (LPC) of silage maize, as a function of various nitrogen rates (0, 50, 100, and 150% of recommended dosage). Results of principal component analysis (PCA) suggested that LPC was highly correlated with leaf greenness index in all developmental stages. In addition, LPC was highly correlated with most of the VIs investigated. A PCA clustering showed the meaningful pattern of N rates. Higher LPC values, VIs, and greenness index were more concentrated in the higher nitrogen (N100% and N150%) sectors. Nitrogen Reflectance Index (NRI) was identified as the most important VIs to monitor and predict LPC in the silage maize field, showing a strong polynomial relationship with LPC in both eight-leaf collar (V8) (R 2 = 0.81, p ≤ 0.01) and tasseling (VT) (R 2 = 0.98, p ≤ 0.001) stages. In addition, among VIs, the Normalized Difference Vegetation Index (NDVI) demonstrated a significant linear regression relationship with LPC (R 2 = 0.80, p ≤ 0.01) in the VT. Findings suggested the high potential of VIs extracted by UAV-taken multispectral imagery and also SPAD proximal sensing to help farmers rapidly diagnose LPC in silage maize, in line with the objectives of precision farming.
Dispersion of a passive scalar around a Taylor bubble

Abstract The motion of Taylor bubbles in capillaries is typical of many engineering and biological systems, ranging from subsurface flows to small-scale reactors. Although the hydrodynamics of elongated bubbles has been the object of several studies, the case where a solute is transported in the surrounding liquid and surface mass-transfer mechanisms act on the solid wall or the bubble–fluid interface is much less understood. To fill this gap, we investigate the transport problem around a confined Taylor bubble to access the competition between advection, diffusion and surface mass transfer in the different regions of the bubble. With this aim, we derive a one-dimensional advection–diffusion–mass-transfer equation where the transport mechanisms are described through an effective velocity, an effective diffusion coefficient and an effective Sherwood number. Our model generalises the Aris–Taylor dispersion to the case of a Taylor bubble and clarifies the impact of surface mass transfer in the advection and diffusion dominated regimes for both the front and rear menisci. The model recovers the typical Pèclet square relationship of the effective diffusion coefficient, which also depends on the film thickness. Also, when the Pèclet number balances with the Sherwood number, there exist conditions that lead to the formation of hot spots of concentration. We show that the typical shape oscillations of the bubble rear locally enhance superficial mass transfer. Finally, we study the transport problem in the uniform film region where the concentration field can be found analytically.
Many-body localization transition with power-law interactions: Statistics of eigenstates

We study spectral and wave-function statistics for many-body localization transition in systems with long-range interactions decaying as $1/{r}^{\ensuremath{\alpha}}$ with an exponent $\ensuremath{\alpha}$ satisfying $d\ensuremath{\le}\ensuremath{\alpha}\ensuremath{\le}2d$, where $d$ is the spatial dimensionality. We refine earlier arguments and show that the system undergoes a localization transition as a function of the rescaled disorder ${W}^{*}=W/{L}^{2d\ensuremath{-}\ensuremath{\alpha}}lnL$, where $W$ is the disorder strength and $L$ the system size. This transition has much in common with that on random regular graphs. We further perform a detailed analysis of the inverse participation ratio (IPR) of many-body wave functions, exploring how ergodic behavior in the delocalized phase switches to fractal one at the critical point and on the localized side of the transition. Our analytical results for the scaling of the critical disorder $W$ with the system size $L$ and for the scaling of IPR in the delocalized and localized phases are supported and corroborated by exact diagonalization of spin chains.
Meta-analysis of early-life antibiotic use and allergic rhinitis

Abstract This meta-analysis aimed to investigate the correlation between early-life antibiotic use and allergic rhinitis. PubMed, Embase, and the Cochrane Central Register of Controlled Trials databases were searched for available studies. Eighteen studies covering 1,768,874 children were included. Early-life antibiotics were associated with an increased incidence of allergic rhinitis (effect size (ES) = 5.00, 95% confidence interval [CI]: 4.88–5.13; I 2 = 95.7%, P heterogeneity <0.001). In Asia, Europe, and the USA, the incidence of allergic rhinitis in the antibiotic group was higher than that in the no medication group (Asia: ES = 3.68, 95% CI: 3.38–4.01; Europe: ES = 3.20, 95% CI: 3.00–3.42; USA: ES = 3.68, 95% CI: 2.74–4.95). Compared with the no medication group, children who received antibiotics in the first 1 week of life (ES = 5.75, 95% CI: 2.18–15.18), first 1 year of life (ES = 3.37, 95% CI: 3.20–3.55; I 2 = 64.2%, P heterogeneity = 0.001), or first 3 years of life (ES = 5.21, 95% CI: 2.42–11.19) had a higher incidence of allergic rhinitis. No individual study influenced the estimates of the meta-analysis. The funnel plot showed moderate symmetry and low publication bias. In conclusion, the use of antibiotics in early life was associated with allergic rhinitis. Still, most included studies analyzed antibiotic exposure as a dichotomous variable, without information on the type and dosage of antibiotics.
Analysis of Antimicrobial Resistance and Genetic Correlations of Escherichia Coli in Dairy Cow Mastitis

Abstract Introduction Escherichia coli is a widespread environmental pathogen frequently causing dairy cow mastitis. This bacterium is particularly capable of acquiring antimicrobial resistance, which can have severe impacts on animal food safety and human health. The objective of the study was to investigate antimicrobial resistance and genetic correlations of E. coli from dairy cow mastitis cases in northern China. Material and Methods Forty strains of E. coli from 196 mastitis milk samples were collected, susceptibility to 13 common antibiotics and the prevalence of resistance genes were tested in these strains, and the genetic characteristics were identified by multilocus sequence typing. Results The results showed that most isolates were multidrug resistant (MDR) (75%), and the resistance rates to cefazolin, trimethoprim-sulfamethoxazole and ampicillin were 77.5%, 55.0%, and 52.5%, respectively. The representative genes of the isolates were aadA (62.5%) and tet(B) (60.0%). Multilocus sequence typing showed 19 different sequence types (STs) and 5 clonal complexes (CCs) in the 40 isolates, mainly represented by ST10 and CC10. The strains of the same ST or CC showed a high level of genetic relatedness, but the characteristics of their antimicrobial resistance were markedly different. Conclusion Most E. coli isolates in the study were MDR strains. Some strains of the same ST or CC showed diverse resistance characteristics to common antimicrobials. Therefore, E. coli from dairy cow mastitis in northern China should be investigated to elucidate its antimicrobial resistance and genotypes.
Galactic evolution of copper in the light of NLTE computations

We have developed a model atom for Cu with which we perform statistical equilibrium computations that allow us to compute the line formation of Cu I lines in stellar atmospheres without assuming Local Thermodynamic Equilibrium (LTE). We validate this model atom by reproducing the observed line profiles of the Sun, Procyon and eleven metal-poor stars. Our sample of stars includes both dwarfs and giants. Over a wide range of stellar parameters we obtain excellent agreement among different Cu I lines. The eleven metal-poor stars have iron abundances in the range -4.2 <= [Fe/H] <= -1.4, the weighted mean of the [Cu/Fe] ratios is -0.22 dex, with a scatter of -0.15 dex. This is very different from the results from LTE analysis (the difference between NLTE and LTE abundances reaches 1 dex) and in spite of the small size of our sample it prompts for a revision of the Galactic evolution of Cu.
What Sets the Massive Star Formation Rates and Efficiencies of Giant Molecular Clouds?

Galactic star formation scaling relations show increased scatter from kpc to sub-kpc scales. Investigating this scatter may hold important clues to how the star formation process evolves in time and space. Here, we combine different molecular gas tracers, different star formation indicators probing distinct populations of massive stars, and knowledge of the evolutionary state of each star-forming region to derive the star formation properties of ∼150 star-forming complexes over the face of the Large Magellanic Cloud (LMC). We find that the rate of massive star formation ramps up when stellar clusters emerge and boost the formation of subsequent generations of massive stars. In addition, we reveal that the star formation efficiency of individual giant molecular clouds (GMCs) declines with increasing cloud gas mass ( ). This trend persists in Galactic star-forming regions and implies higher molecular gas depletion times for larger GMCs. We compare the star formation efficiency per freefall time ( ) with predictions from various widely used analytical star formation models. While these models can produce large dispersions in similar to those in observations, the origin of the model-predicted scatter is inconsistent with observations. Moreover, all models fail to reproduce the observed decline of with increasing in the LMC and the Milky Way. We conclude that analytical star formation models idealizing global turbulence levels and cloud densities and assuming a stationary star formation rate (SFR) are inconsistent with observations from modern data sets tracing massive star formation on individual cloud scales. Instead, we reiterate the importance of local stellar feedback in shaping the properties of GMCs and setting their massive SFR.
The interaction between predictability and pre-boundary lengthening on syllable duration in Taiwan Southern Min

Abstract This study investigated how predictability and prosodic phrasing interact in accounting for the variability of syllable duration in Taiwan Southern Min. Speech data were extracted from 8 hours of spontaneous speech. Three predictability measurements were examined: bigram surprisal, bigram informativity, and lexical frequency. Results showed that higher informativity and surprisal led to longer syllables. As for the interaction with prosodic positions, there was a general weakening of predictability effects for syllables closer to the boundary, especially in the pre-boundary position, where pre-boundary lengthening was the strongest. However, the effect of word informativity appeared to be least modulated by this effect of boundary marking. These findings are consistent with a hypothesis that prosodic structure modulates the predictability effects on phonetic variability. The robustness of informativity in predicting syllable duration also suggests a possibility of stored phonetic variants associated with a word's usual contextual predictability.
Electronic anisotropies revealed by detwinned ARPES measurements of FeSe

We report high resolution ARPES measurements of detwinned FeSe single crystals. The application of a mechanical strain is used to promote the volume fraction of one of the orthorhombic domains in the sample, which we estimate to be 80$\%$ detwinned. While the full structure of the electron pockets consisting of two crossed ellipses may be observed in the tetragonal phase at temperatures above 90~K, we find that remarkably, only one peanut-shaped electron pocket oriented along the longer $a$ axis contributes to the ARPES measurement at low temperatures in the nematic phase, with the expected pocket along $b$ being not observed. Thus the low temperature Fermi surface of FeSe as experimentally determined by ARPES consists of one elliptical hole pocket and one orthogonally-oriented peanut-shaped electron pocket. Our measurements clarify the long-standing controversies over the interpretation of ARPES measurements of FeSe.