Miracle-dz/UniFAR-FTUs huggingface dataset:
Keywords to success: a practical guide to maximise the visibility and impact of academic papers

In a growing digital landscape, enhancing the discoverability and resonance of scientific articles is essential. Here, we offer ten recommendations to amplify the discoverability of studies in scientific databases. Particularly, we argue that the strategic use and placement of key terms in the title, abstract, and keyword sections can boost indexing and appeal. By surveying 237 journals in ecology and evolutionary biology, we found that current author guidelines may unintentionally limit article discoverability. Our survey of 5842 studies revealed that authors frequently exhaust abstract word limits — particularly those capped under 250 words. This suggests that current guidelines may be overly restrictive and not optimised to increase the dissemination and discoverability of digital publications. Additionally, 91.9% of studies used redundant keywords in the title or abstract, undermining optimal indexing in databases. We encourage adopting structured abstracts to maximise the incorporation of key terms in titles, abstracts, and keywords. In addition, we encourage the relaxation of abstract and keyword limitations in journals with strict guidelines, and the inclusion of multilingual abstracts to broaden global accessibility. These evidence-based recommendations to editors are designed to improve article engagement and facilitate evidence synthesis, thereby aligning scientific publishing with the modern needs of academic research.
Advancements in Human Breast Organoid Culture: Modeling Complex Tissue Structures and Developmental Insights

Organoids have been widely used for studying tissue growth and modeling diseases, but achieving physiologically relevant architecture, size, and function has remained a challenge. Here, we develop a next-generation organotypic culture method that enables the formation of a highly patterned, complex, branched tissue that is spatially organized to accurately recapitulate the morphology, scale, cellular, transcriptional, and tissue-level heterogeneity of human breast tissue. Hormone responsiveness of organoids is also a feature allowing for examination of androgen therapy or post-menopausal changes to breast tissue development and regeneration. Live imaging allows for studying stem cell dynamics during organoid formation and is adaptable to a high throughput setting. Real-time imaging of organoid formation reveals activation of latent epithelial organogenesis programs and inductive cellular dynamics that drive formation of a miniature breast tissue along with its mesenchyme akin to tissue stroma. By advancing human breast organoid technology, this model can elucidate cell- and tissue-level consequences to hormonal changes and therapy. In addition, this method can lead to new insights into the cellular, molecular, and tissue-level processes involved in organogenesis and regeneration, as well as disease.
Antiepileptic medication strengthens excitatory neurotransmission in pyramidal neurons of the adult human neocortex

Homeostatic synaptic plasticity serves to maintain neuronal function within a dynamic range upon perturbations in network activity. While coordinated structural and functional changes at synaptic sites play a crucial role in adaptive processes, the specific regulatory mechanisms and biological relevance of homeostatic plasticity in the human brain warrant further investigation. In this study, we investigated the effects of neural network silencing, achieved through pharmacological inhibition of voltage-gated sodium channels or glutamatergic neurotransmission – common targets of antiepileptic medication – on functional and structural properties of murine and human cortical tissue. Using mouse entorhino-hippocampal tissue cultures, acute neocortical slices of adult mice, and human brain tissue, we characterize homeostatic synaptic plasticity across models, brain regions, and species. Our findings demonstrate local homeostatic synaptic plasticity in the adult human neocortex, highlighting the potential effects of antiepileptic medication in brain regions unaffected by the primary diseases, which might represent a mechanism for neuropsychiatric effects linked to these medications and increased seizure susceptibility upon discontinuation of antiepileptic medication.
Diffusion of activated ATM explains γH2AX and MDC1 spread beyond the DNA damage site

During DNA repair, ATM-induced H2AX histone phosphorylation and MDC1 recruitment spread megabases beyond the damage site. The mechanism underlying this spread remains unclear. To elucidate this step of the DNA damage response, we measured ATM and MDC1 recruitment kinetics at micro-irradiation-induced complex DNA lesions. While MDC1 spreads beyond the damage-induced focus of ATM accumulation, cohesin loader NIPBL and cohesin subunit RAD21 accumulate later than MDC1. Such delayed recruitment suggests that, in complex DNA lesions, loop extrusion cannot alone explain γH2AX and MDC1 spread. To determine if diffusion of activated ATM could account for the observed behavior, we measured the exchange rate and diffusion constants of ATM and MDC1 within damaged and unperturbed chromatin. Based on these measurements, we introduced a quantitative model that describes the dynamic behavior of ATM and subsequent MDC1 spread at complex DNA lesions. Altogether, we propose activated ATM diffusion as an underlying mechanism of MDC1 spread.
Genetic or pharmacological inactivation of CREBBP sensitizes B-cell Acute Lymphoblastic Leukemia to Ferroptotic Cell Death upon BCL2 Inhibition

B-cell acute lymphoblastic leukemia (B-ALL) is a leading cause of death in childhood and outcomes in adults remain dismal. There is therefore an urgent clinical need for therapies that target the highest risk cases. Mutations in the histone acetyltransferase CREBBP associate with high-risk features in B-ALL and have been implicated in chemoresistance. We performed a targeted drug screen in isogenic human cell lines, identifying a number of actionable small molecules that specifically target CREBBP-mutated B-ALL. The most potent was the BCL2 inhibitor Venetoclax, which acts through a non-canonical mechanism resulting in ferroptotic cell death. CREBBP-mutated cell lines showed differences in cell-cycle, metabolism and response to oxidative stress. Lastly, we demonstrate that small-molecule inhibition of CREBBP sensitizes B-ALL cells, regardless of genotype, to Venetoclax-induced ferroptosis in-vitro and in-vivo, providing a potential novel drug combination for broader clinical translation in B-ALL.
Developing Nematode Management Zones Using Soil EC Data

Much research work is ongoing that is investigating methods and tools for delineating cotton root knot nematode [M. incognita] management zones via soil texture. Apparent soil electrical conductivity (ECa) can be a surrogate measurement for determining soil texture as clay content is a dominant physical property affecting ECa. The Veris 3100 soil EC sensor is often used to obtain near-continuous ECa data across fields. Research from Midwest and Plains states indicates correlations between ECa and soil texture have varies widely. Results from two years of comparisons of Veris soil ECa data to actual soil texture from nine south Georgia cotton fields indicated deep (0-90 cm) ECa values provided by the Veris 3100 sensor compared reasonably well to lab texture results. Correlations (r) between % sand and/or % clay and ECa for deep depth (0-90 cm) were between 0.31 and 0.94 for 2006 and 0.35 to 0.90 for 2005. The Veris sensor proved useful for creating soil zones in the fields involved in this study in 2005/2006.
Smallholder's Market Participation, Characteristics, Tractor Use Implication and Determinants in Nigeria

ABSTRACT:The smallholders' transformation from the passive nature of farming using crude implements into active and rewarding market participation remains problematic. Over the years, smallholder farming and market have been associated with trails of many middlemen, farm gate sales, and consumption. However, there is a lack of information on the recent changes that have occurred due to government intervention, globalization, and the fourth industrial revolution in Nigeria and many other developing countries. Hence, this study investigates the smallholders' market participation, level of participation, determinants of the smallholders' market participation, driving force behind the smallholder farming, tractor-use, and tractor-use impact on the smallholders' market participation. A multistage sampling technique was applied to select 280 smallholder farmers for the research. Descriptive statistics and propensity score matching (PMS) were used to analyze the data. The findings revealed that the smallholders' market participation level is low. Most smallholders (62 percent) could not sell up to 50 percent of their products. The study showed that smallholders participate in the market at different output sales levels, with most tractor-users participating in the market at a high output sales level. In contrast, many non-tractor users participated at low output sales, indicating poor market participation. Most smallholders sell their produce at the farm gate to date. Smallholder tractor use remains low. Only 28 percent of the total respondents used tractors. The PSM analysis revealed that tractor-use significantly impacts the smallholders' market participants alongside the education level, area cultivated, and access to information on farming activities such as market information. The smallholders' involvement in another occupation negatively impacts their market participation. The year(s) of farming experience did not positively impact the smallholder's market participation, which could be due to poor adoption of technology. The farming and market participation seems unchanged, with most individuals, not collective smallholders, not frequently producing for the market. Their market participation has not experienced meaningful progress despite income generation forming the major motive behind their farming. It is true that some smallholders mainly produce for consumption, but the sole purpose of ensuring households' food security is the least reason many households embark on farming. Positive transformation of the smallholder into active and mainstream market participation is possible if technology, including tractor, and suitable approaches are adopted, as evidenced by this research among smallholders who used tractors. It was recommended that the smallholders' tractor-use issue be prioritized in all policies addressing the smallholders' market participation. Smallholders should not be viewed as farmers who only went into farming for consumption.
The Effect of R&D on Economic Growth: Evidence from Cross-Country Panel Data

ABSTRACT:Research and Development (R&D) in particular and innovation in general are the best drivers of long-run sustainable economic growth and spending in R&D has been recognized as one of the means to the economic accomplishment of both developed and developing countries. However, the sustainability of the association between R&D & economic growth exclusively depends upon the performance of other macroeconomic determinants. Hence, we examine the effect of R&D on economic growth in the existence of five important macroeconomic determinants. The study focusses on 151 selected countries, including both developed and developing, over the period 1996-2020 and focused on the macro-level analysis. Our research is based on the deployment of system Generalized Method of Moments (GMM) technique. The GMM technique encompasses longitudinal data; and is gathered by pooling time-series across a pool of cross-sectional units, specifically country-wise. The biggest advantage of using the GMM technique is that it allows for addressing the endogeneity problem using lagged explanatory variables as instrumental variables. We have three samples in our analysis, namely pool of developed countries, pool of developing countries, and the pool of both developed and developing countries. Our study deploys four proxies of R&D to work the nexus between R&D and economic growth in presence of five important macroeconomic determinants. In each of these three samples, we run four GMM models, depending upon the deployment of four R&D activities that we used in this study. We observe an evidence that R&D plays a significant role in economic growth when the selected macroeconomic determinants are used as control variables. Our findings are mostly robust to R&D specifications, country specifications, model specifications, and estimation strategies. Most importantly, the effect of lagged economic growth is highly positive, signifying that economies with higher economic growth in the previous years tend to grow faster in presence of R&D intensity. The behaviour of other macroeconomic variables is having diverse nature. Some of these variables behave positively, while others behave negatively. This study suggests that policymakers should focus on improving R&D activities and strengthening the chosen macroeconomic determinants to facilitate economic development. Hence, it contributes to the ongoing policy debate, that is with reference to R&D effect on economic growth, on the finest strategies for attaining sustainable economic growth in all these selected countries. Hence, we can use these findings to develop a targeted policy that promotes R&D intensity in the phases of economic development.
The Effect of Phonotactics on Alternation Learning

This study investigates whether alternation learning is facilitated by a matching phonotactic generalization. In a series of artificial grammar learning experiments, English learners were trained on artificial languages evincing categorical vowel harmony alternations across morpheme boundaries. These languages differed in the degree of harmony within stems (disharmonic, semiharmonic, and harmonic), and thus the degree of phonotactic support for the alternation. Results indicate that alternation learning was best when supported by matching stem phonotactics (harmonic language; experiment 1). Learners, however, were reluctant to extend a learned phonotactic constraint to novel unseen alternations (experiments 2 and 3). Taken together, the results are consistent with the hypothesis that alternation learning is facilitated by a matching static phonotactic generalization, but that learners are conservative in positing alternations in the absence of overt evidence for them.
Spring snow cover extent reductions in the 2008–2012 period exceeding climate model projections

Analysis of Northern Hemisphere spring terrestrial snow cover extent (SCE) from the NOAA snow chart Climate Data Record (CDR) for the April to June period (when snow cover is mainly located over the Arctic) has revealed statistically significant reductions in May and June SCE. Successive records for the lowest June SCE have been set each year for Eurasia since 2008, and in 3 of the past 5 years for North America. The rate of loss of June snow cover extent between 1979 and 2011 (−17.8% decade−1) is greater than the loss of September sea ice extent (−10.6% decade−1) over the same period. Analysis of Coupled Model Intercomparison Project Phase 5 (CMIP5) model output shows the marked reductions in June SCE observed since 2005 fall below the zone of model consensus defined by +/−1 standard deviation from the multi‐model ensemble mean.
Toward improving the reliability of hydrologic prediction: Model structure uncertainty and its quantification using ensemble‐based genetic programming framework

Uncertainty analysis is starting to be widely acknowledged as an integral part of hydrological modeling. The conventional treatment of uncertainty analysis in hydrologic modeling is to assume a deterministic model structure, and treat its associated parameters as imperfectly known, thereby neglecting the uncertainty associated with the model structure. In this paper, a modeling framework that can explicitly account for the effect of model structure uncertainty has been proposed. The modeling framework is based on initially generating different realizations of the original data set using a non‐parametric bootstrap method, and then exploiting the ability of the self‐organizing algorithms, namely genetic programming, to evolve their own model structure for each of the resampled data sets. The resulting ensemble of models is then used to quantify the uncertainty associated with the model structure. The performance of the proposed modeling framework is analyzed with regards to its ability in characterizing the evapotranspiration process at the Southwest Sand Storage facility, located near Ft. McMurray, Alberta. Eddy‐covariance‐measured actual evapotranspiration is modeled as a function of net radiation, air temperature, ground temperature, relative humidity, and wind speed. Investigating the relation between model complexity, prediction accuracy, and uncertainty, two sets of experiments were carried out by varying the level of mathematical operators that can be used to define the predictand‐predictor relationship. While the first set uses just the additive operators, the second set uses both the additive and the multiplicative operators to define the predictand‐predictor relationship. The results suggest that increasing the model complexity may lead to better prediction accuracy but at an expense of increasing uncertainty. Compared to the model parameter uncertainty, the relative contribution of model structure uncertainty to the predictive uncertainty of a model is shown to be more important. Furthermore, the study advocates that the search to find the optimal model could be replaced by the quest to unearth possible models for characterizing hydrological processes.
Public Parks: The Key to Livable Communities

Everything that anybody (whether they are citizen activists, or public officials, or professional landscape architects, architects, and planners) needs to know about the critical role public parks play in creating livable communities. Millions of dollars are being spent on restoring parks and creating new ones. Planner Alexander Garvin explains the rationales for their existence, the forms they take, their value, ways to pay for and govern them, and the ingredients that make successful parks, providing the first single definitive source of wisdom about them.
Background Versus Risk-Based Screening Levels - An Examination Of Arsenic Background Soil Concentrations In Seven States

Arsenic is often present in soils naturally or from historical anthropogenic activities. Arsenic is commonly a constituent of potential concern at environmental remediation sites, even where there is no reason to suspect a release. Site risks are frequently driven by arsenic, and risk-based screening levels below background are not uncommon. However, determining whether arsenic concentrations are consistent with background typically requires an extensive background data set. The ability to gain access to representative background locations owned by third parties is problematic at best in any characterization study. Consequently, many sites undergo characterization and potentially remediation for arsenic concentrations in soil that may in reality be representative of background (natural or anthropogenic). This study examines a large soil arsenic background data set to provide insight on typical concentrations of arsenic that are naturally occurring or represent anthropogenic background. Between 1995 and 2001, over 1,600 background soil samples were collected from 189 sites in Kentucky, Maryland, New York, Ohio, Pennsylvania, Virginia, and West Virginia. Samples were collected using strict Quality Assurance/Quality Control procedures under a United States Environmental Protection Agency (USEPA) Superfund Administrative Order on Consent (AOC) and were analyzed by USEPA-approved laboratories. All data were verified and 10% underwent detailed data validation. Arsenic concentrations in samples retained for statistical analysis ranged from 1.1 mg/kg to 89 mg/kg. Data are evaluated by state and by geology and are compared to USEPA and state risk-based screening levels (RBSLs). Some standard background threshold values (BTVs) are derived for each state and distinct geology. The BTVs are greater than RBSLs. This extensive, regional data set should be considered by all stakeholders involved in relevant risk-based decisions related to arsenic in soils. The consideration of this data set and the BTVs may aid in the appropriate identification of arsenic in soils below typical background concentrations. In turn, the use of BTVs may aid in identifying where risks are truly elevated relative to background, and thus where remediation may or may not be appropriate.
Cross faults in extensional settings: Stress triggering, displacement localization, and implications for the origin of blunt troughs at Valles Marineris, Mars

[1] Motivated by conflicting interpretations concerning the origin of blunt terminations of troughs at Valles Marineris, Mars, we investigate the reactivation of preexisting cross faults in response to stress changes associated with slippage along a major, basin-bounding normal fault (i.e., border fault). Coulomb stress changes indicate that cross fault reactivation is possible in both the footwalls and hanging walls of border faults, although this is dependent on the distance between the border and cross faults. Cross faults accommodate dip-slip normal motion for most border fault geometries and conditions we tested, but strike-slip motions are predicted when preexisting cross faults are vertical. Furthermore, although lateral extensions of the border fault (LEBFs) may nucleate within cross fault footwalls at all stages of border fault development, they are favored to develop when border faults and cross fault tip lines are proximal. Observations from the Valles Marineris extensional province, Mars, are consistent with (1) normal displacements along cross faults, (2) numerous examples of pit-chains, interpreted to represent surface expressions of lateral extensions to the border fault (LEBF), (3) the lack of well-developed cross faults in the footwall of border faults, and (4) the inference that tapered trough ends formed in areas that lacked cross faults prior to the main phase of extension at Valles Marineris. We suggest a new sequence of deformation that accounts for the formation of blunt-trough terminations during the major phase of Valles Marineris extension: coeval and locally bidirectional extension, that results from local stress field changes associated with border fault growth in a dominantly unidirectional remote (extensional) strain field. By implication, many of the irregular closed troughs such as Hebes Chasma are better interpreted as grabens rather than collapse depressions.
On the estimation of seismic intensity in earthquake early warning systems

The infamous Tokai Earthquake, which by some accounts is overdue, is expected to be a magnitude ≥ 8 event that will cause unprecedented damage in regions of Japan. To mitigate hazards from large earthquakes in Japan, an earthquake early warning (EEW) system was developed that is able to determine hypocentral locations from P‐arrival data within a few seconds and then transmit this information before the onset of large ground motions from the later arrival of S‐waves. We introduced a new source parameter for seismic intensity magnitude that can be estimated from the real‐time P‐wave data during the early stages of fault rupture for most earthquakes M ≥ 6.5. The use of this parameter results in a significant improvement in the uncertainty in the estimated seismic intensity compared to estimates derived from earthquake magnitude. A pre‐established relation between the P‐ and S‐wave seismic intensity therefore enables an EEW system to issue a rapid and reasonably reliable prediction of the amount of ground shaking that may be expected from the damaging S‐waves.