Miracle-dz/UniFAR-FTUs huggingface dataset:
Neem oil against Aeromonas hydrophila infection by disrupting quorum sensing and biofilm formation

Abstract Aeromonas hydrophila is an opportunistic pathogen that can cause a number of infectious diseases in fish and is widely distributed in aquatic environments. Antibiotics are the main approach against A. hydrophila infections, while the emergence of resistant bacteria limits the application of antibiotics. Here, quorum-sensing (QS) was defined as the target and the inhibitory effects of neem oil against QS of A. hydrophila was studied. The results showed that neem oil could dose-dependently reduce aerolysin, protease, lipase, acyl-homoserine lactones (AHLs), biofilm and swarming motility at sub-inhibitory concentrations. Results of real-time PCR demonstrated that neem oil could down-regulate the transcription of aerA, ahyI and ahyR. Moreover, neem oil showed significant protections to A549 cells and a fish infection model. Taken together, these results indicated that neem oil could be chosen as a promising candidate for the treatment of A. hydrophila infections.
Nunchaku: optimally partitioning data into piece-wise contiguous segments

Abstract Motivation When analyzing 1D time series, scientists are often interested in identifying regions where one variable depends linearly on the other. Typically, they use an ad hoc and therefore often subjective method to do so. Results Here, we develop a statistically rigorous, Bayesian approach to infer the optimal partitioning of a dataset not only into contiguous piece-wise linear segments, but also into contiguous segments described by linear combinations of arbitrary basis functions. We therefore present a general solution to the problem of identifying discontinuous change points. Focusing on microbial growth, we use the algorithm to find the range of optical density where this density is linearly proportional to the number of cells and to automatically find the regions of exponential growth for both Escherichia coli and Saccharomyces cerevisiae. For budding yeast, we consequently are able to infer the Monod constant for growth on fructose. Our algorithm lends itself to automation and high throughput studies, increases reproducibility, and should facilitate data analyses for a broad range of scientists. Availability and implementation The corresponding Python package, entitled Nunchaku, is available at PyPI: https://pypi.org/project/nunchaku.
The impact of air pollution on hospitalization for COPD patients in China

Abstract Background With the rapid development of the global economy and the acceleration of urbanization, air pollution has become a major environmental problem threatening human health. There is limited evidence on the acute effects of air pollution on chronic obstructive pulmonary disease (COPD). Methods From 2014 to 2019, we collected data on daily admissions for COPD patients from a city in China. We used the generalized additive model together with distributed lag models to fit the associations of air pollutants with hospital admissions. Results We observed significant increments in the number of daily admissions (0.086–0.109%) for COPD for a unit range increase in air quality index, PM2.5 and PM10 over four lag days. The impact of air pollution on the number of daily admissions was mainly reflected in the COPD patients who were hospitalized through outpatient departments and tertiary hospitals. Discussion Short-term exposure to outdoor air pollution may induce the occurrence or exacerbation of COPD patients; therefore, government departments should strengthen the management of air pollution, improve supervision and control mechanisms, pay attention to the quality of medical services, and reduce the adverse effects of air pollution on patients' health.
The Effect of Moving Meditation Exercise on Depression and Sleep Quality of the Elderly

The aim of the present study was to determine the effect of moving meditation exercise training on depression level and sleep quality in the elderly individuals. The present research was carried out between March 2021 and June 2022 as a randomized control group pretest-posttest design. The study sample consisted of 114 individuals 65 years of age and older who were registered to 2 family health centers in a province. The Introductory Information Form, Katz Activities of Daily Living Scale (Katz ADL), International Physical Activity Questionnaire, Geriatric Depression Scale, and Pittsburgh Sleep Quality Index were used in the study. After the 6-week intervention, participants who were in the moving meditation exercise group had lower depression and had good Sleep Quality intensity scores than those in the control group. The average depression, subjective sleep quality, sleep disorder, and overall sleep quality significantly decreased over time in the moving meditation exercise group (P < .05). Moving meditation exercise may reduce the depression level of elderly individuals and improve sleep quality. These results show that moving meditation exercise can be a supportive method for traditional treatments. In consideration of the growing health economy, it may be recommended that nurses incorporate this exercise in their professional practice and compare long-term results with other complementary practices in the Turkish population.
Demonstrating soil moisture remote sensing with observations from the UK TechDemoSat‐1 satellite mission

The ability of spaceborne Global Navigation Satellite System (GNSS) bistatic radar receivers to sense changes in soil moisture is investigated using observations from the low Earth orbiting UK TechDemoSat‐1 satellite (TDS‐1). Previous studies using receivers on aircraft or towers have shown that ground‐reflected GNSS signals are sensitive to changes in soil moisture, though the ability to sense this variable from space has yet to be quantified. Data from TDS‐1 show a 7 dB sensitivity of reflected signals to temporal changes in soil moisture. If the effects of surface roughness and vegetation on the reflected signals can be quantified, spaceborne GNSS bistatic radar receivers could provide soil moisture on relatively small spatial and temporal scales.
Unlocking the Goldenseal Reveals the Complexities of Natural Product–Drug Interactions

This is signi ﬁ cant for goldenseal, as the reversible CYP3A inhibitory effect by an unknown phyto-constituent was unmasked with higher doses of goldenseal. As Nguyen et al., did not attempt to identify the unknown goldenseal phytoconstituent in this manuscript, we believe that it should be a future priority to better understand goldenseal – drug interactions. In addition, previous reports from PBPK modeling show noteworthy interactions between goldenseal and ABCB1 transporters (Adiwidjaja et al., 2022). We suggest conducting clinical pharmacokinetic studies to assess the impact of inhibiting intestinal ABCB1 on the bioavailability of ABCB1 substrates to identify or exclude additional interactions of concern. The ﬁ nal limitation that should be addressed is the inclusion of more diverse populations in future clinical and PBPK modeling studies to recognize and subsequently highlight situations that are therapeutically safe or pose the highest risk for clinically signi ﬁ cant goldenseal – drug interactions
Antimicrobial susceptibility of U.S. porcine Brachyspira isolates and genetic diversity of B. hyodysenteriae by multilocus sequence typing

Swine dysentery, caused by Brachyspira hyodysenteriae and the newly recognized Brachyspira hampsonii in grower-finisher pigs, is a substantial economic burden in many swine-rearing countries. Antimicrobial therapy is the only commercially available measure to control and prevent Brachyspira-related colitis. However, data on antimicrobial susceptibility trends and genetic diversity of Brachyspira species from North America is limited. We evaluated the antimicrobial susceptibility profiles of U.S. Brachyspira isolates recovered between 2013 and 2022 to tiamulin, tylvalosin, lincomycin, doxycycline, bacitracin, and tylosin. In addition, we performed multilocus sequence typing (MLST) on 64 B. hyodysenteriae isolates. Overall, no distinct alterations in the susceptibility patterns over time were observed among Brachyspira species. However, resistance to the commonly used antimicrobials was seen sporadically with a higher resistance frequency to tylosin compared to other tested drugs. B. hampsonii was more susceptible to the tested drugs than B. hyodysenteriae and B. pilosicoli. MLST revealed 16 different sequence types (STs) among the 64 B. hyodysenteriae isolates tested, of which 5 STs were previously known, whereas 11 were novel. Most isolates belonged to the known STs: ST93 (n = 32) and ST107 (n = 13). Our findings indicate an overall low prevalence of resistance to clinically important antimicrobials other than tylosin and bacitracin, and high genetic diversity among the clinical Brachyspira isolates from pigs in the United States during the past decade. Further molecular, epidemiologic, and surveillance studies are needed to better understand the infection dynamics of Brachyspira on swine farms and to help develop effective control measures.
The Moralisation of Tourism: Sun, Sand... and Saving the World?

Introduction Chapter 1. Mass Tourism Chapter 2. What's new? - Traveller, tourist and the moral debate Chapter 3. The hosts: fragile places, fragile people? Chapter 4. The tourists: too much freedom? Chapter 5. The cultural sensibilities of the New Moral Tourist Chapter 6. Travelling for a change: global culture and the ethical tourist Chapter 7. The moralisation of tourism, the Third World and development, Postscript: Re-presenting tourism
Pretreatment characteristics associated with symptom reduction during group cognitive processing therapy versus exposure therapy for PTSD: an exploratory study of Veterans

ABSTRACT Exposure and cognitive-based therapies are both effective for PTSD, but knowledge of which intervention is best for which patient is lacking. This lack of knowledge is particularly noticeable for group treatments, as no study has examined whether responses to different group therapies are associated with different pretreatment characteristics. Here, we explored whether pretreatment levels of three types of psychological characteristics—PTSD symptom clusters, posttraumatic cognitions, and emotion regulation difficulties—were associated with symptom reduction during group-delivered cognitive versus exposure-based PTSD treatment. Participants were Veterans with PTSD drawn from two previous clinical trials: one of group CPT (GCPT; n = 32) and the other of group-based exposure therapy (GBET; n = 21). Growth curve modeling was used to identify pretreatment variables that predicted weekly PTSD symptom changes during each therapy. Higher posttraumatic cognitions at pretreatment predicted steeper PTSD symptom reduction during GCPT but not GBET. Additionally, symptom reduction during each therapy was associated with different pretreatment emotion regulation difficulties: difficulties with goal-directed behavior for GBET and lack of emotional clarity and limited access to emotion regulation strategies for GCPT. These findings suggest that assigning Veterans to a group PTSD therapy that better matches their pretreatment psychological profile might facilitate a better therapeutic response.
The Bleeding Time Test in 2024: A Glorious Past and Current Challenges

The bleeding time has been used in clinical practice for more than 100 years, with the highest number of measurements performed between 1970 and 2000 worldwide. The bleeding time is measured largely using the approach by Mielke et al, 1 mostly with commercially available devices (e.g., Simplate and Surgicutt) based on the assay introduced by Mielke et al, 1 in which standardized skin incisions are performed on a forearm with the use of a sphygmomanometer cuff to maintain a standard venous pressure. The bleeding time test was long consideredasthebestclinicallyavailablecomprehensivetestto assess primary hemostasis, especially platelet function. This test once represented a clinically useful test in two clinical settings: (1) diagnostic evaluation of patients suspected of plateletdisordersordefectsinplatelet – vesselwallinteractions; and (2) prediction of clinically relevant bleeding in patients undergoing invasive procedures, in particular major surgery. From a perspective of basic science research, thebleeding time was a valuable model of microvascular injury used to assess primaryhemostasisatthesiteofplugformationasafunctionof time including the determination of multiple substances, especially platelet-derived factors involving thromboxane B2, platelet factor 4, β -thromboglobulin, soluble CD40 ligand, and P-selectin. 2,3 Within bleeding time blood samples typically collected every 30 to 60seconds, small amounts or concentrations of various blood coagulation proteins such as coagulation factors (factors II, V, XIII, ﬁ brinogen) and coagulation inhibitors (protein C) have been determined using
MEETING PHOSPHORUS REQUIREMENTS OF RUMINANTS IN AN ENVIRONMENTALLY RESPONSIBLE WAY

Public scrutiny of the impact of agricultural practices on the environment is growing. The livestock and poultry industries have been targeted for attention because of their visibility, and for real as well as perceived abuses. Large concentrations of animals in relatively small areas create difficult challenges in terms of odor and nutrient management, but problems of nutrient management can plague small as well as large animal operations. One of the fundamental challenges facing the livestock/feed industries is to recycle the flow of feed nutrients, particularly phosphorus (P) and nitrogen (N), from animal operations back to cropland where they can again be used for crop production. Anything short of this is not sustainable, and will ultimately be unacceptable to the broader public.
GPT-4V(ision) Unsuitable for Clinical Care and Education: A Clinician-Evaluated Assessment

OpenAI's large multimodal model, GPT-4V(ision), was recently developed for general image interpretation. However, less is known about its capabilities with medical image interpretation and diagnosis. Board-certified physicians and senior residents assessed GPT-4V's proficiency across a range of medical conditions using imaging modalities such as CT scans, MRIs, ECGs, and clinical photographs. Although GPT-4V is able to identify and explain medical images, its diagnostic accuracy and clinical decision-making abilities are poor, posing risks to patient safety. Despite the potential that large language models may have in enhancing medical education and delivery, the current limitations of GPT-4V in interpreting medical images reinforces the importance of appropriate caution when using it for clinical decision-making.
‘Return to Europe’ Revisited - The Role of Manele and Chalga in Constructing Norms and Counter-elites in Romania and Bulgaria

The ‘return’ to Europe of eight post-Communist countries situated in Central and Eastern Europe (hereinafter CEE: Poland, Hungary, the Czech Republic, Slovakia, Slovenia, Estonia, Latvia, Lithuania) started with the fall of the Communist regime in 1989 and ended in May 2004 with their accession to the European Union (EU). For the CEE countries EU accession represented more than joining a community of welfare and stability, it entailed also the need to be recognized as part of the same cultural and historical family. At the same time, the EU’s decision to actively contribute to the transformation of political and economic stabilization in CEE led to the elaboration of strategies, instruments and mechanisms meant to strengthen the new-born democracies. Hereby, enlargement was considered one of the EU’s most efficient tools in increasing its political, economic and geostrategic sphere of influence in Eastern Europe. However, the EU’s involvement in the region went beyond that of a strategic actor and of a financial donor helping the CEE countries to strengthen their economy and infrastructure. Throughout the membership criteria, known as the Copenhagen Criteria, the EU presented its normative character as reflected in the promotion of its own norms and ‘ways of doing things’ in CEE. The success story of the EU enlargement round in 2004 and EU impact on inducing normative policies through the use of conditionality has been widely acknowledged by scholars of European integration (Grabbe 2006; Vachudova 2005). The main consensus was built on the idea that the EU represented:
The effects of inhaled corticosteroids on healthy airways

Rationale: The effects of inhaled corticosteroids (ICS) on healthy airways are poorly defined. Objectives: To delineate the effects of ICS on gene expression in healthy airways, without confounding caused by changes in disease-related genes and disease-related alterations in ICS-responsiveness. Methods: Randomised open-label bronchoscopy study of high dose ICS therapy in 30 healthy adult volunteers randomised 2:1 to i) fluticasone propionate 500 mcg bd or ii) no treatment, for 4 weeks. Laboratory staff were blinded to allocation. Biopsies and brushings were analysed by immunohistochemistry, bulk RNA sequencing, DNA methylation array and metagenomics. Measurements and main results: ICS induced small between-group differences in blood and lamina propria eosinophil numbers, but not in other immunopathological features, blood neutrophils, FeNO, FEV1, microbiome or DNA methylation. ICS treatment upregulated 72 genes in brushings and 53 genes in biopsies, and downregulated 82 genes in brushings and 416 genes in biopsies. The most downregulated genes in both tissues were canonical markers of type-2 inflammation (FCER1A, CPA3, IL33, CLEC10A, SERPINB10 and CCR5), T cell-mediated adaptive immunity (TARP, TRBC1, TRBC2, PTPN22, TRAC, CD2, CD8A, HLA-DQB2, CD96, PTPN7), B cell immunity (CD20, immunoglobulin heavy and light chains), and innate immunity, including CD48, Hobit, RANTES, Langerin and GFI1. An IL-17-dependent gene signature was not upregulated by ICS. Conclusions: In healthy airways, 4-week ICS exposure reduces gene expression related to both innate and adaptive immunity, and reduces markers of type-2 inflammation. This implies that homeostasis in health involves tonic type-2 signalling in the airway mucosa, which is exquisitely sensitive to ICS.
Spoken Word2Vec: A Perspective And Some Techniques

Text word embeddings that encode distributional semantic features work by modeling contextual similarities of frequently occurring words. Acoustic word embeddings, on the other hand, typically encode low-level phonetic similarities. Semantic embeddings for spoken words have been previously explored using similar algorithms to Word2Vec, but the resulting vectors still mainly encoded phonetic rather than semantic features. In this paper, we examine the assumptions and architectures used in previous works and show experimentally how Word2Vec algorithms fail to encode distributional semantics when the input units are acoustically correlated. In addition, previous works relied on the simplifying assumptions of perfect word segmentation and clustering by word type. Given these conditions, a trivial solution identical to text-based embeddings has been overlooked. We follow this simpler path using automatic word type clustering and examine the effects on the resulting embeddings, highlighting the true challenges in this task.
Holographic torus correlators in $\text{AdS}_3$ gravity coupled to scalar field

This paper investigates holographic torus correlators of generic operators at conformal infinity and a finite cutoff within AdS$_3$ gravity coupled with a free scalar field. Using a near-boundary analysis and solving the gravitational boundary value problem, we solve Einstein's equation and calculate mixed correlators for massless and massive coupled scalar fields. The conformal ward identity on the torus has been reproduced holographically, which can be regarded as a consistency check. Further, recurrence relations for a specific class of higher-point correlators are derived, validating AdS$_3$/CFT$_2$ with non-trivial boundary topology. While the two-point scalar correlator is accurately computed on the thermal AdS$_3$ saddle, the higher-point correlators associated with scalar and stress tensor operators are explored.
Enhancing Deformable Object Manipulation By Using Interactive Perception and Assistive Tools

In the field of robotic manipulation, the proficiency of deformable object manipulation lags behind human capabilities due to the inherent characteristics of deformable objects. These objects have infinite degrees of freedom, resulting in non-trivial perception and state estimation, and complex dynamics, complicating the prediction of future configurations. Although recent research has focused on deformable object manipulation, most approaches rely on static vision and simple manipulation techniques, limiting the performance level. This paper proposes two solutions to enhance the performance: interactive perception and the use of assistive tools. The first solution posits that optimal perspectives exist during deformable object manipulation, facilitating easier state estimation. By exploring the action-perception regularity, interactive perception facilitates better manipulation and perception. The second solution advocates for the use of assistive tools, a hallmark of human intelligence, to improve manipulation performance. For instance, a folding board can aid in garment folding tasks by reducing object deformation and managing complex dynamics. Hence, this research aims to address the deformable object manipulation problem by incorporating interactive perception and assistive tools to augment manipulation performance.
A Simple and Powerful Framework for Stable Dynamic Network Embedding

In this paper, we address the problem of dynamic network embedding, that is, representing the nodes of a dynamic network as evolving vectors within a low-dimensional space. While the field of static network embedding is wide and established, the field of dynamic network embedding is comparatively in its infancy. We propose that a wide class of established static network embedding methods can be used to produce interpretable and powerful dynamic network embeddings when they are applied to the dilated unfolded adjacency matrix. We provide a theoretical guarantee that, regardless of embedding dimension, these unfolded methods will produce stable embeddings, meaning that nodes with identical latent behaviour will be exchangeable, regardless of their position in time or space. We additionally define a hypothesis testing framework which can be used to evaluate the quality of a dynamic network embedding by testing for planted structure in simulated networks. Using this, we demonstrate that, even in trivial cases, unstable methods are often either conservative or encode incorrect structure. In contrast, we demonstrate that our suite of stable unfolded methods are not only more interpretable but also more powerful in comparison to their unstable counterparts.
Online Optimization for Network Resource Allocation and Comparison with Reinforcement Learning Techniques

We tackle in this paper an online network resource allocation problem with job transfers. The network is composed of many servers connected by communication links. The system operates in discrete time; at each time slot, the administrator reserves resources at servers for future job requests, and a cost is incurred for the reservations made. Then, after receptions, the jobs may be transferred between the servers to best accommodate the demands. This incurs an additional transport cost. Finally, if a job request cannot be satisfied, there is a violation that engenders a cost to pay for the blocked job. We propose a randomized online algorithm based on the exponentially weighted method. We prove that our algorithm enjoys a sub-linear in time regret, which indicates that the algorithm is adapting and learning from its experiences and is becoming more efficient in its decision-making as it accumulates more data. Moreover, we test the performance of our algorithm on artificial data and compare it against a reinforcement learning method where we show that our proposed method outperforms the latter.