Miracle-dz/UniFAR-FTUs huggingface dataset:
The Association Between Rainfall, Temperature, and Reported Drinking Water Source: A Multi‐Country Analysis

Climate change may alter access to safe drinking water, with important implications for health. We assessed the relationship between temperature and rainfall and utilization of basic drinking water (BDW) in The Gambia, Mozambique, Pakistan, and Kenya. The outcomes of interest were (a) whether the reported drinking water source used in the past 2 weeks met the World Health Organization definition of BDW and (b) use of a BDW source that was always available. Temperature and precipitation data were compiled from weather stations and satellite data and summarized to account for long‐ and short‐term weather patterns and lags. We utilized random forests and logistic regression to identify key weather variables that predicted outcomes by site and the association between important weather variables and BDW use. Higher temperatures were associated with decreased BDW use at three of four sites and decreased use of BDW that is always available at all four sites. Increasing rainfall, both in the long‐ and short‐term, was associated with increased BDW use in three sites. We found evidence for interactions between household wealth and weather variables at two sites, suggesting lower wealth populations may be more sensitive to weather‐driven changes in water access. Changes in temperature and precipitation can alter safe water use in low‐resource settings—investigating drivers for these relationships can inform efforts to build climate resilience.
Quantum critical behavior in itinerant electron systems: Eliashberg theory and instability of a ferromagnetic quantum critical point

We consider the problem of fermions interacting with gapless long-wavelength collective bosonic modes. The theory describes, among other cases, a ferromagnetic quantum-critical point (QCP) and a QCP towards nematic ordering. We construct a controllable expansion at the QCP in two steps: we first create a non-Fermi-liquid ``zero-order'' Eliashberg-type theory, and then demonstrate that the residual interaction effects are small. We prove that this approach is justified under two conditions: the interaction should be smaller than the fermionic bandwidth, and either the band mass ${m}_{B}$ should be much smaller than $m={k}_{F}∕{v}_{F}$, or the number of fermionic flavors $N$ should be large. For an SU(2) symmetric ferromagnetic QCP, we find that the Eliashberg theory itself includes a set of singular renormalizations which can be understood as a consequence of an effective long-range dynamic interaction between quasiparticles, generated by the Landau damping term. These singular renormalizations give rise to a negative nonanalytic ${q}^{3∕2}$ correction to the static spin susceptibility, and destroy a ferromagnetic QCP. We demonstrate that this effect can be understood in the framework of the ${\ensuremath{\phi}}^{4}$ theory of quantum criticality. We also show that the nonanalytic ${q}^{3∕2}$ correction to the bosonic propagator is specific to the SU(2) symmetric case. For systems with a scalar order parameter, the ${q}^{3∕2}$ contributions from individual diagrams cancel out in the full expression of the susceptibility, and the QCP remains stable.
Profound Impact of Decline in N-Acetylgalactosamine-4-Sulfatase (Arylsulfatase B) on Molecular Pathophysiology and Human Diseases

The enzyme N-acetylgalactosamine-4-sulfatase (Arylsulfatase B; ARSB) was originally identified as a lysosomal enzyme which was deficient in Mucopolysaccharidosis VI (MPS VI; Maroteaux-Lamy Syndrome). The newly directed attention to the impact of ARSB in human pathobiology indicates a broader, more pervasive effect, encompassing roles as a tumor suppressor, transcriptional mediator, redox switch, and regulator of intracellular and extracellular-cell signaling. By controlling the degradation of chondroitin 4-sulfate and dermatan sulfate by removal or failure to remove the 4-sulfate residue at the non-reducing end of the sulfated glycosaminoglycan chain, ARSB modifies the binding or release of critical molecules into the cell milieu. These molecules, such as galectin-3 and SHP-2, in turn, influence crucial cellular processes and events which determine cell fate. Identification of ARSB at the cell membrane and in the nucleus expands perception of the potential impact of decline in ARSB activity. The regulation of availability of sulfate from chondroitin 4-sulfate and dermatan sulfate may also affect sulfate assimilation and production of vital molecules, including glutathione and cysteine. Increased attention to ARSB in mammalian cells may help to integrate and deepen our understanding of diverse biological phenomenon and to approach human diseases with new insights.
Assessment of the Role of Free-Living and Farmed Fallow Deer (Dama dama) as A Potential Source of Human Infection with Multiple-Drug-Resistant Strains of Yersinia enterocolitica and Yersinia pseudotuberculosis

Yersinia enterocolitica and Y. pseudotuberculosis are Gram-negative, facultative anaerobic bacteria that cause yersiniosis—one of the most important zoonotic diseases of the digestive tract. The aim of this study was to determine the prevalence of potentially human-pathogenic Y. enterocolitica and Y. pseudotuberculosis strains in free-living and farmed fallow deer, and to evaluate their sensitivity to chemotherapeutics. A total of 372 rectal swabs were analyzed, including 262 from free-living and 110 from farmed fallow deer. Due to the psychrophilic properties of Yersinia, two samples were collected from each animal. Seven Y. enterocolitica strains were isolated from free-living fallow deer, while two strains were isolated from farmed fallow deer. Yersinia pseudotuberculosis strains were not identified. All isolated Y. enterocolitica strains were ystB-positive, and phylogenetic analysis based on the nucleotide sequences of this gene revealed the presence of two phylogenetic groups. Yersinia enterocolitica strains isolated from fallow deer belonged to biotype 1A, and serotyping analysis demonstrated that the vast majority did not agglutinate with any diagnostic sera. All strains were multiple drug resistant and were not sensitive to at least four of the tested chemotherapeutics (amoxicillin with clavulanic acid, ampicillin, cefalexin, and streptomycin). One Y. enterocolitica strain isolated from a free-living animal was resistant to nine out of the 13 analyzed chemotherapeutics and was intermediately sensitive to the four remaining chemotherapeutics. The highest sensitivity was noted in case of ciprofloxacin (five strains) and trimethoprim-sulfamethoxazole (three strains). Only one strain isolated from a free-living animal was sensitive to three out of the 13 examined antibiotics, whereas the remaining strains were sensitive to only one drug or were not sensitive to any of the chemotherapeutics used. The results of this study indicate that multiple drug-resistant Y. enterocolitica strains can be carried by free-living and farmed fallow deer. This observation gives serious cause for concern because the meat of fallow deer and other ruminants is often consumed semi-raw (steak) or raw (tartar steak).
Let’s Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms

Modeling what makes a request persuasive - eliciting the desired response from a reader - is critical to the study of propaganda, behavioral economics, and advertising. Yet current models can’t quantify the persuasiveness of requests or extract successful persuasive strategies. Building on theories of persuasion, we propose a neural network to quantify persuasiveness and identify the persuasive strategies in advocacy requests. Our semi-supervised hierarchical neural network model is supervised by the number of people persuaded to take actions and partially supervised at the sentence level with human-labeled rhetorical strategies. Our method outperforms several baselines, uncovers persuasive strategies - offering increased interpretability of persuasive speech - and has applications for other situations with document-level supervision but only partial sentence supervision.
Entanglement scaling of excited states in large one-dimensional many-body localized systems

We study the properties of excited states in one-dimensional many-body localized (MBL) systems using a matrix product state algorithm. First, the method is tested for a large disordered non-interacting system, where for comparison we compute a quasi-exact reference solution via a Monte Carlo sampling of the single-particle levels. Thereafter, we present extensive data obtained for large interacting systems of L~100 sites and large bond dimensions chi~1700, which allows us to quantitatively analyze the scaling behavior of the entanglement S in the system. The MBL phase is characterized by a logarithmic growth (L)~log(L) over a large scale separating the regimes where volume and area laws hold. We check the validity of the eigenstate thermalization hypothesis. Our results are consistent with the existence of a mobility edge.
An Anomaly Detection Framework for Twitter Data

An anomaly indicates something unusual, related to detecting a sudden behavior change, and is also helpful in detecting irregular and malicious behavior. Anomaly detection identifies unusual events, suspicious objects, or observations that differ significantly from normal behavior or patterns. Discrepancies in data can be observed in different ways, such as outliers, standard deviation, and noise. Anomaly detection helps us understand the emergence of specific diseases based on health-related tweets. This paper aims to analyze tweets to detect the unusual emergence of healthcare-related tweets, especially pre-COVID-19 and during COVID-19. After pre-processing, this work collected more than 44 thousand tweets and performed topic modeling. Non-negative matrix factorization (NMF) and latent Dirichlet allocation (LDA) were deployed for topic modeling, and a query set was designed based on resultant topics. This query set was used for anomaly detection using a sentence transformer. K-means was also employed for clustering outlier tweets from the cleaned tweets based on similarity. Finally, an unusual cluster was selected to identify pandemic-like healthcare emergencies. Experimental results show that the proposed framework can detect a sudden rise of unusual tweets unrelated to regular tweets. The new framework was employed in two case studies for anomaly detection and performed with 78.57% and 70.19% accuracy.
A novel family of small molecule HIF-1 alpha stabilizers for the treatment of diabetic wounds; an integrated in silico, in vitro, and in vivo strategy

Hypoxia-inducible factor-1 alpha (HIF-1α) is a crucial regulator of wound healing, which includes epithelialization, angiogenesis, granulation, tissue development, and wound contraction. Even though diabetic wounds are hypoxic, HIF-1α levels are decreased during healing. Diabetic wound healing necessitates the modulation of hypoxia-induced responses by VHL–HIF-1α protein–protein inhibition. Our proposed hypothesis is to increase HIF-1α levels by inhibiting VHL and HIF-1α interactions by novel small bioactive molecules, accelerating diabetic wound healing. A three features (two aromatic rings and one hydrogen bond acceptor) pharmacophore hypothesis was generated from the existing HIF-1α modulators. Virtual screening was done based on the generated pharmacophore, and a library consisting of the top 20 out of 3728 compounds was selected using ZINCPharmer. Of the top 20 molecules, the pyrazole moiety was identified as the top “HIT”. Five analogues of pyrazole were designed, and Scifinder ascertained the novelty. The designed compounds were synthesized and characterized by IR, Mass, and NMR. Preliminarily, we have carried out a scratch wound assay using 3T3L1 cell lines. All the synthesized compounds showed significant wound healing activity. Further, to validate the in vitro assay, the compound CI, which showed effective in vitro results was used for in vivo study. Using the diabetes mouse model, comprising streptozotocin-induced (STZ) diabetic mice and scratch wound assay, we demonstrated that inhibiting the VHL and HIF-1α connection is a promising strategy for treating diabetic ulcers. Molecules CI and CP were found to have substantial in silico, in vitro, and in vivo outcomes.
Residues of functions of Cayley-Dickson variables and Fermat's last theorem

Function theory of Cayley-Dickson variables is applied to Fermat's last theorem. For this the homotopy theorem, Rouch\'e's theorem and residues of meromorphic functions over Cayley-Dickson algebras are used. A special meromorphic function of Cayley-Dickson variables is constructed and its properties are investigated.
Psychological distress and coping styles among baccalaureate nursing students: Promoting mental health of future nurses in COVID-19 pandemic

BACKGROUND: Coronavirus disease (COVID-19) causes significant psychological distress among nursing students. College-bound nursing students might have preferred different types of coping strategies to deal with psychological distress. This study aims to measure the psychological distress and role of coping styles to mediate the stress level among the baccalaureate nursing students amid the COVID-19 pandemic. MATERIAL AND METHODS: A cross-sectional online survey was conducted in December 2020 at a nursing college attached to a tertiary care teaching hospital, North India. Nearly 251 baccalaureate nursing students completed the Impact of Event Scale-Revised (IES-R) and Coping Orientation to Problems Experienced Inventory (Brief-COPE) scale to report their psychological distress and coping styles, respectively. Chi-square test, independent sample t-test followed by binary and multivariable regression were used to identify the factors associated with distress in students during the pandemic. RESULTS: Students' mean age was 22.22 ± 1.24 years. The mean IES-R was 19.59 ± 12.45 in nursing students. Psychological distress found a significant association with age (P = 0.022), academic class (P = 0.016), travel history (P = 0.034), and being positive reverse transcription–polymerase chain reaction (RT-PCR) for COVID-19 status of self (P = 0.018) and family members in the medical profession (P = 0.013). In binary logistic regression, stress level found a significant association with first-year academic level (OR: 3.250, 95% CI: 1.429–7.390, P = 0.005) and family members in the medical profession (OR: 4.44, 95% CI: 1.019–19.382, P = 0.047). Adaptive coping styles were more frequently preferred than maladaptive coping styles (54% vs 41%). Adaptive (r = 0.295, P < 0.001) and maladaptive coping (r = 0.403, P < 0.001) shows a significant positive relationship with stress in students, respectively. CONCLUSIONS: Coronavirus pandemic causes significant distress among nursing students. Students were able to manage stress using acceptance and religious/spiritual coping strategies. During the pandemic, stress management to support mental health is highly recommended.
Image-to-Image Translation-Based Data Augmentation for Improving Crop/Weed Classification Models for Precision Agriculture Applications

Applications of deep-learning models in machine visions for crop/weed identification have remarkably upgraded the authenticity of precise weed management. However, compelling data are required to obtain the desired result from this highly data-driven operation. This study aims to curtail the effort needed to prepare very large image datasets by creating artificial images of maize (Zea mays) and four common weeds (i.e., Charlock, Fat Hen, Shepherd’s Purse, and small-flowered Cranesbill) through conditional Generative Adversarial Networks (cGANs). The fidelity of these synthetic images was tested through t-distributed stochastic neighbor embedding (t-SNE) visualization plots of real and artificial images of each class. The reliability of this method as a data augmentation technique was validated through classification results based on the transfer learning of a pre-defined convolutional neural network (CNN) architecture—the AlexNet; the feature extraction method came from the deepest pooling layer of the same network. Machine learning models based on a support vector machine (SVM) and linear discriminant analysis (LDA) were trained using these feature vectors. The F1 scores of the transfer learning model increased from 0.97 to 0.99, when additionally supported by an artificial dataset. Similarly, in the case of the feature extraction technique, the classification F1-scores increased from 0.93 to 0.96 for SVM and from 0.94 to 0.96 for the LDA model. The results show that image augmentation using generative adversarial networks (GANs) can improve the performance of crop/weed classification models with the added advantage of reduced time and manpower. Furthermore, it has demonstrated that generative networks could be a great tool for deep-learning applications in agriculture.
Staying with the Trouble with Wilderness: Reworking Nature and Culture in the Plantationocene

This essay analyzes the use of fire on Upland Island Wilderness Area (UIW) to examine how postindustrial wilderness sites rework operative notions of nature, wildness, and preservation within U.S. environmental thinking and politics. Postindustrial wilderness areas complicate conceptualizations of nature as pristine, unspoiled, or even beautiful, challenging us to address biodiversity and ecosystem function in ways that are less centered on human(ist) values. An intensively managed pine plantation prior to wilderness designation, UIW blatantly transgresses liberal humanist boundaries of nature and culture, ecology and industry. I draw on feminist and posthumanist theory to demonstrate how contestations surrounding the use of fire on UIW resituate ethical and epistemic implications of wilderness management, offering a critical counterpoint to the prioritization of pristine nature within U.S. environmental politics and demanding less humanist approaches to the complex forest ecologies of the plantationocene.
Alloying Iron into Palladium Nanoparticles for an Efficient Catalyst in Acetylene Dicarbonylation

Motivated by the prominent catalytic performance and durability of nanoalloy catalysts, the Pd-based bimetallic nanoalloy catalysts were prepared using an aqueous reduction method. The Fe-Pd bimetallic nanoalloy catalyst (nano-Fe/Pd) demonstrated 98.4% yield and 99.7% selectivity for the unsaturated 1,4-dicarboxylic acid diesters. Moreover, the inductively coupled plasma (ICP) analysis shows that the Pd leaching of the catalyst can be effectively suppressed by alloying Fe atoms into the Pd crystal lattice for acetylene dicarbonylation. The detailed catalyst structure and morphology characterization demonstrate that introducing Fe into the Pd nanoparticles tunes the electronic–geometrical properties of the catalyst. Theoretical calculations indicate that the electrons of Fe transfer to Pd in the nano-Fe/Pd catalyst, enhancing activation of the C≡C bond in acetylene and weakening CO absorption capacity on catalyst surfaces. Alloying Fe into the Pd nanocatalyst effectively inhibits active metal leaching and improves catalyst activity and stability under high-pressure CO reactions.
DNA Sequence and Structure under the Prism of Group Theory and Algebraic Surfaces

Taking a DNA sequence, a word with letters/bases A, T, G and C, as the relation between the generators of an infinite group π, one can discriminate between two important families: (i) the cardinality structure for conjugacy classes of subgroups of π is that of a free group on one to four bases, and the DNA word, viewed as a substitution sequence, is aperiodic; (ii) the cardinality structure for conjugacy classes of subgroups of π is not that of a free group, the sequence is generally not aperiodic and topological properties of π have to be determined differently. The two cases rely on DNA conformations such as A-DNA, B-DNA, Z-DNA, G-quadruplexes, etc. We found a few salient results: Z-DNA, when involved in transcription, replication and regulation in a healthy situation, implies (i). The sequence of telomeric repeats comprising three distinct bases most of the time satisfies (i). For two-base sequences in the free case (i) or non-free case (ii), the topology of π may be found in terms of the SL(2,C) character variety of π and the attached algebraic surfaces. The linking of two unknotted curves—the Hopf link—may occur in the topology of π in cases of biological importance, in telomeres, G-quadruplexes, hairpins and junctions, a feature that we already found in the context of models of topological quantum computing. For three- and four-base sequences, other knotting configurations are noticed and a building block of the topology is the four-punctured sphere. Our methods have the potential to discriminate between potential diseases associated to the sequences.
Excitation Functions of Neutron Threshold Reactions on Some Isotopes of Germanium, Arsenic, and Selenium in the 6.3- to 14.7-MeV Energy Range

Cross sections were measured for the [sup 75]As(n,p)[sup 75]Ge, [sup 75]As(n,[alpha])[sup 72]Ga, [sup 75]As(n,2n)[sup 74]As, [sup 74,76,78]Se(n,p)[sup 74],[sup 76],[sup 78]As, [sup 78],[sup 80]Se(n,[alpha])[sup 75],[sup 77]Ge, [sup 72,73,74]Ge(n,p)[sup 72,73,74]Ga, and [sup 70,76]Ge(n,2n)[sup 69,75]Ge reactions over the 6.3- to 14.7-MeV neutron energy range. Samples of As[sub 2]O[sub 3], selenium, and germanium or GeO[sub 2] of natural isotopic abundance were used. The neutrons were produced via the D(d,n)[sup 3] He reaction using a deuterium gas target at a variable energy cyclotron (E[sub n] = 6.3 to 11.9 MeV) and via the T(d,n)[sup 4] He reaction using a solid titanium-tritium target at a neutron generator (E[sub n] = 14.7 MeV). The activation technique was used in combination with high-resolution gamma-ray spectroscopy. The experimental excitation functions are well reproduced by the nuclear model calculations, based on statistical multistep reaction theory.
Impact of Imidacloprid Resistance on the Demographic Traits and Expressions of Associated Genes in Aphis gossypii Glover

Imidacloprid is one of the most widely used neonicotinoid insecticides to control sap-sucking insect pests, including Aphis gossypii. The intensive application of chemical insecticides to A. gossypii led to the development of resistance against several insecticides, including imidacloprid. Therefore, it is crucial to understand the association between imidacloprid resistance and the fitness of A. gossypii to limit the spread of the resistant population under field contexts. In this study, we used the age-stage, two-sex life table method to comprehensively investigate the fitness of imidacloprid resistant (ImR) and susceptible strains (SS) of melon aphids. Results showed that ImR aphids have prolonged developmental stages and decreased longevity, fecundity, and reproductive days. The key demographic parameters (r, λ, and R0) were significantly reduced in ImR strain compared to SS aphids. Additionally, the molecular mechanism for fitness costs was investigated by comparing the expression profile of juvenile hormone-binding protein (JHBP), juvenile hormone epoxide hydrolase (JHEH), juvenile hormone acid O-methyltransferase (JHAMT), Vitellogenin (Vg), ecdysone receptor (EcR), and ultraspiracle protein (USP) supposed to be associated with development and reproduction in insects. The results of RT-qPCR showed that EcR, JHBP, JHAMT, JHEH, and Vg genes were downregulated, while USP was statistically the same in ImR A. gossypii compared to the SS strain. Together, these results provide in-depth information about the occurrence and magnitude of fitness costs against imidacloprid resistance that could help manage the evolution and spread of A. gossypii resistance in field populations