Miracle-dz/UniFAR-FTUs huggingface dataset:
Steric interactions and out-of-equilibrium processes control the internal organization of bacteria

Significance Self-organizing processes and structures lie at the heart of biology. Here, we study compaction and localization of DNA inside cells of the bacterium Escherichia coli. We explain the physics behind this phenomenon based on excluded-volume interactions, by which the DNA spontaneously phase separates from other intracellular crowders. We then explore the rich phenomenology that arises when these interactions are coupled to out-of-equilibrium cellular processes, such as the transcription and degradation of messenger RNAs. The results agree well with recent experimental observations of the size, positioning, and dynamical splitting of E. coli’s DNA, providing an example of the tight control that cells exert over their internal organization by means of out-of-equilibrium processes. Despite the absence of a membrane-enclosed nucleus, the bacterial DNA is typically condensed into a compact body—the nucleoid. This compaction influences the localization and dynamics of many cellular processes including transcription, translation, and cell division. Here, we develop a model that takes into account steric interactions among the components of the Escherichia coli transcriptional–translational machinery (TTM) and out-of-equilibrium effects of messenger RNA (mRNA) transcription, translation, and degradation, to explain many observed features of the nucleoid. We show that steric effects, due to the different molecular shapes of the TTM components, are sufficient to drive equilibrium phase separation of the DNA, explaining the formation and size of the nucleoid. In addition, we show that the observed positioning of the nucleoid at midcell is due to the out-of-equilibrium process of mRNA synthesis and degradation: mRNAs apply a pressure on both sides of the nucleoid, localizing it to midcell. We demonstrate that, as the cell grows, the production of these mRNAs is responsible for the nucleoid splitting into two lobes and for their well-known positioning to 1/4 and 3/4 positions on the long cell axis. Finally, our model quantitatively accounts for the observed expansion of the nucleoid when the pool of cytoplasmic mRNAs is depleted. Overall, our study suggests that steric interactions and out-of-equilibrium effects of the TTM are key drivers of the internal spatial organization of bacterial cells.
Misinfo Belief Frames: A Case Study on Covid & Climate News

Prior beliefs of readers impact the way in which they project meaning onto news headlines. These beliefs can influence their perception of news reliability, as well as their reaction to news, and their likelihood of spreading the misinformation through social networks. However, most prior work focuses on fact-checking veracity of news or stylometry rather than measuring impact of misinformation. We propose Misinfo Belief Frames, a formalism for understanding how readers perceive the reliability of news and the impact of misinformation. We also introduce the Misinfo Belief Frames (MBF) corpus, a dataset of 66k inferences over 23.5k headlines. Misinformation frames use commonsense reasoning to uncover implications of real and fake news headlines focused on global crises: the Covid-19 pandemic and climate change. Our results using large-scale language modeling to predict misinformation frames show that machine-generated inferences can influence readers' trust in news headlines (readers' trust in news headlines was affected in 29.3% of cases). This demonstrates the potential effectiveness of using generated frames to counter misinformation.
SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Metal Forming Stress Field with Attention Mechanism and Perceptual Features

In stress field analysis, the finite element method is a crucial approach, in which the mesh-density has a significant impact on the results. High mesh density usually contributes authentic to simulation results but costs more computing resources, leading to curtailing efficiency. To eliminate this drawback, we propose a new data-driven mesh-density boost model named SuperMeshingNet that strengthens the advantages of finite element analysis (FEA) with low mesh-density as inputs to the deep learning model, which consisting of Res-UNet architecture, to acquire high-density stress field instantaneously, shortening computing time and cost automatically. Moreover, the attention mechanism and the perceptual features are utilized, enhancing the performance of SuperMeshingNet. Compared with the baseline that applied the linear interpolation method, SuperMeshingNet achieves a prominent reduction in the mean squared error (MSE) and mean absolute error (MAE) on the test data, which contains prior unseen cases. Based on the dataset of the plane stress fields in sheet metal forming, the comparative experiments are proceeded to demonstrate the high quality and superior precision of the reconstructed results generated by the proposed model. The well-trained model can successfully show more excellent performance than the baseline models on the multiple scaled mesh-density, including 2×, 4×, and 8×. Enhanced by SuperMeshingNet with broaden scaling of mesh density and high precision output, FEA can be accelerated with seldom computational time and cost. We publicly share our work with full detail of implementation at https://github.com/zhenguonie/2021_SuperMeshing_2D_Metal_Forming.
Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context

BERT-based text ranking models have dramatically advanced the state-of-the-art in ad-hoc retrieval, wherein most models tend to consider individual query-document pairs independently. In the mean time, the importance and usefulness to consider the cross-documents interactions and the query-specific characteristics in a ranking model have been repeatedly confirmed, mostly in the context of learning to rank. The BERT-based ranking model, however, has not been able to fully incorporate these two types of ranking context, thereby ignoring the inter-document relationships from the ranking and the differences among queries. To mitigate this gap, in this work, an end-to-end transformer-based ranking model, named Co-BERT, has been proposed to exploit several BERT architectures to calibrate the query-document representations using pseudo relevance feedback before modeling the relevance of a group of documents jointly. Extensive experiments on two standard test collections confirm the effectiveness of the proposed model in improving the performance of text re-ranking over strong fine-tuned BERT-Base baselines. We plan to make our implementation open source to enable further comparisons.
Fluctuation and dissipation within a deformed holographic model at finite temperature and chemical potential

In this work we study fluctuations and dissipation of a string in a deformed anti-de Sitter (AdS) space at finite temperature and density. The deformed AdS space is a charged black hole solution of the Einstein-Maxwell-Dilaton action. In this background we take into account the backreaction on the horizon function from an exponential deformation of the AdS space. In this set up, we study the equations of motion of the string from the Nambu-Goto action. The motion of the string endpoint describes holographically the fluctuations and dissipation of a particle moving in a four-dimensional Minkowski space. From this model we compute the admittance and study its behavior against the temperature for some values of the chemical potential. In order to obtain the two-point correlations functions, and the mean square displacement we consider separately the bosonic and fermionic cases with $\mu 0$, respectively. From the regularized mean square displacements of the bosonic and fermionic cases we obtain the short and large time approximations. For the short time case we find the usual ballistic regime, and for the long time case we obtain a sub-diffusive regime characteristic of a ultra-slow scaled Brownian motion. Combining the results from the admittance and mean square displacements we also study the fluctuation-dissipation theorem.
Chaotic antiferromagnetic nano-oscillator driven by spin torque

We theoretically describe the behavior of a terahertz nano-oscillator based on an anisotropic antiferromagnetic dynamical element driven by spin torque. We consider the situation when the polarization of the spin-current is perpendicular to the external magnetic field applied along the anisotropy easy-axis. We determine the domain of the parametric space (field, current) where the oscillator demonstrates chaotic dynamics. Characteristics of the chaotic regimes are analyzed using conventional techniques such as spectra of the Lyapunov exponents. We show that the threshold current of the chaos appearance is particularly low in the vicinity of the spin-flop transition. In this regime, we consider the mechanism of the chaos appearance in detail when the field is fixed and the current density increases. We show that the appearance of chaos is preceded by a regime of quasiperiodic dynamics on the surface of a two-frequency torus arising in phase space as a result of the Neimark-Sacker bifurcation.
A GENERALISATION OF HIGHER-RANK GRAPHS

Abstract We introduce ‘generalised higher-rank k-graphs’ as a class of categories equipped with a notion of size. They extend not only higher-rank k-graphs, but also the Levi categories introduced by the first author as a categorical setting for graphs of groups. We prove that examples of generalised higher-rank k-graphs can be constructed using Zappa–Szép products of groupoids and higher-rank graphs.
FiG-NeRF: Figure-Ground Neural Radiance Fields for 3D Object Category Modelling

We investigate the use of Neural Radiance Fields (NeRF) to learn high quality 3D object category models from collections of input images. In contrast to previous work, we are able to do this whilst simultaneously separating foreground objects from their varying backgrounds. We achieve this via a 2-component NeRF model, FiG-NeRF, that prefers explanation of the scene as a geometrically constant background and a deformable foreground that represents the object category. We show that this method can learn accurate 3D object category models using only photometric supervision and casually captured images of the objects. Additionally, our 2-part decomposition allows the model to perform accurate and crisp amodal segmentation. We quantitatively evaluate our method with view synthesis and image fidelity metrics, using synthetic, lab-captured, and in-the-wild data. Our results demonstrate convincing 3D object category modelling that exceed the performance of existing methods.
Physical, chemical, mechanical, and micromorphological characterization of dental needles

Background In anesthetic techniques, touching bones can cause needle bending. Theoretically, a needle should support such deflection without fracturing. However, it is possible that a needle may fracture depending on the quality and type of needle used. This study evaluated the physical, chemical, and micromorphological characteristics of long and short dental anesthetic needles, as well as the mechanical properties of flexural load and bending resistance when needles are subjected to different bending angles. Methods Long and short needles (30G, Jets, Misawa, Selekto, Terumo, Unoject and 27G, Dencojet, Injex, Jets, Misawa, Procare, Setoject XL, Terumo) were evaluated. Scanning electron microscopy was used to evaluate the needle bevels and energy-dispersive X-ray spectroscopy was used for the chemical analysis of needle compositions. Flexural loading and bending strength assessments were performed using a universal testing machine by bending the needles (n = 5) to angles of 30°, 60°, or 90°, or until fracture occurred. Results The Injex 27G, Jets 27G, and Septoject XL 27G needles were all less than 30 mm in length. There were small percentage variations in the chemical compositions of the needles. Superior smoothness was observed for the Unoject 30G needle, which exhibited the highest fracture resistance at 60°. The Jets 30G needle exhibited greater resistance to fractures at 90°. The Procare 27G needle exhibited the highest load resistance to bending, followed by the Septoject XL 27G needle, and both needles were tied for the lowest fracture resistance. No needle fractured when bent to 30° or at less than three bends to 60° or 90°. Conclusions Greater needle resistance to bending increases the probability of early fracturing. Thinner and shorter needles are more resistant than longer and thicker needles. Performing a single bend does not result in any significant risk of fracture or obliterate the lumen, allowing for the continued passage of anesthetic liquid.
Development of a Joint Prediction Model Based on Both the Radiomics and Clinical Factors for Predicting the Tumor Response to Neoadjuvant Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer

Purpose Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for locally advanced rectal cancer (LARC). However, the accuracy of traditional clinical indicators in predicting tumor response is poor. Recently, radiomics based on magnetic resonance imaging (MRI) has been regarded as a promising noninvasive assessment method. The present study was conducted to develop a model to predict the pathological response by analyzing the quantitative features of MRI and clinical risk factors, which might predict the therapeutic effects in patients with LARC as accurately as possible before treatment. Patients and Methods A total of 82 patients with LARC were enrolled as the training cohort and internal validation cohort. The pre-CRT MRI after pretreatment was acquired to extract texture features, which was finally selected through the minimum redundancy maximum relevance (mRMR) algorithm. A support vector machine (SVM) was used as a classifier to classify different tumor responses. A joint radiomics model combined with clinical risk factors was then developed and evaluated by receiver operating characteristic (ROC) curves. External validation was performed with 107 patients from another center to evaluate the applicability of the model. Results Twenty top image texture features were extracted from 6192 extracted-radiomic features. The radiomics model based on high-spatial-resolution T2-weighted imaging (HR-T2WI) and contrast-enhanced T1-weighted imaging (T1+C) demonstrated an area under the curve (AUC) of 0.8910 (0.8114–0.9706) and 0.8938 (0.8084–0.9792), respectively. The AUC value rose to 0.9371 (0.8751–0.9997) and 0.9113 (0.8449–0.9776), respectively, when the circumferential resection margin (CRM) and carbohydrate antigen 19-9 (CA19-9) levels were incorporated. Clinical usefulness was confirmed in an external validation cohort as well (AUC, 0.6413 and 0.6818). Conclusion Our study indicated that the joint radiomics prediction model combined with clinical risk factors showed good predictive ability regarding the treatment response of tumors as accurately as possible before treatment.
Electroacupuncture Ameliorates Depressive-Like Behaviors in Poststroke Rats via Activating the tPA/BDNF/TrkB Pathway

Background Electroacupuncture (EA) is a form of physical therapy that has been widely used in clinical practice in China. Post-stroke depression (PSD) is the most common neuropsychiatric complication after stroke. EA has been shown to have beneficial effects on PSD patients. However, the potential mechanism underlying the protective effects of EA on PSD remains unclear. Here, we investigated whether tissue plasminogen activator (tPA)/brain-derived neurotrophic factor (BDNF)/tyrosine kinase receptor B (TrkB) signaling pathway participates in the therapeutic effects of EA in a rat PSD model. Methods Experimental PSD was induced by combining middle cerebral artery occlusion (MCAO) with chronic unpredictable mild stimulation (CUMS) in adult male rats. Bodyweight gain, neurological score, sucrose preference, and open field test were determined at 0, 7, 14, and 35 days after completing MCAO. The protein expressions of tPA, precursor BDNF (proBDNF), mature BDNF (mBDNF), and TrkB were measured by immunofluorescence and Western blot analysis. The tPA inhibitor plasminogen inhibitor-1 (PAI-1) was used to explore whether tPA plays a crucial role in the protective effects of EA on PSD. Results Compared with the sham rats, the PSD rats showed decreased bodyweight, deteriorated neurological score, and significant depressive-like behaviors. EA remarkably reversed bodyweight loss, neurological deficit, and depressive-like behaviors in PSD rats. Immunofluorescence staining and Western blot analysis showed that PSD-induced decreased expression of tPA, mBDNF, and TrkB were prevented by EA. Furthermore, we found that the effects of EA against PSD-induced depressive-like behaviors were abolished by PAI-1, the specific inhibitor of tPA. Conclusion Our results suggest that the improvement in depressive-like behaviors induced by EA is likely achieved via activation of the tPA/BDNF/TrkB pathway.
Self-Assembly Iron Oxide Nanoclusters for Photothermal-Mediated Synergistic Chemo/Chemodynamic Therapy

Methods Superparamagnetic iron oxide nanoclusters (SPIOCs) were located within the core, which resulted in high photothermal conversion and outstanding generation of reactive oxygen species (ROS). The shell consisted of a human serum albumin- (HSA-) paclitaxel (PTX) layer, which extended the blood circulation time and ensured the effectiveness of the chemotherapy. Arg-Gly-Asp peptides (RGD) were linked to the naked cysteine moieties in HSA to promote the specific targeting of human glioma U87 cells by αvβ3 integrins. Continuous near-infrared light irradiation triggered and promoted the synergistic chemo/CDT therapy through the photothermal effect. Results Our SPIOCs@HSA-RGD nanoplatform showed well biocompatibility and could target glioma specifically. Photothermal conversion and ROS burst were detected after continuous 808 nm light irradiation, and a significant antitumor effect was achieved. Conclusion Experimental in vitro and in vivo evaluations showed that our photothermal-mediated chemo/CDT therapy could efficiently inhibit tumor growth and is therefore promising for cancer therapy.
Anaemia and Related Nutritional Deficiencies in Chinese Patients with Obesity, 12 Months Following Laparoscopic Sleeve Gastrectomy

Background Laparoscopic sleeve gastrectomy (LSG) has become a predominant bariatric procedure at present. However, data are scarce regarding the nutritional impact of this procedure on Chinese patients. This study aimed to evaluate the prevalence of nutritional deficiency after LSG in Chinese patients. Methods Eighty-two patients with obesity were recruited from the Second Xiangya Hospital of Central South University, and all patients underwent LSG and completed the visit. Results Compared with the baseline, the serum albumin levels increased significantly at 1–12 months (P<0.001) after surgery, and the hypoalbuminemia rate decreased from 8.5% to 0% throughout the study (P=0.063). Anaemia was present in 7.3% of all patients before surgery, and its prevalence increased to 11.0% at 12 months post-operation (P=0.109). The anaemia rate of fertile females was higher than that of males (21.4% vs 2.3%, P=0.036). No significant changes were found in vitamin B12 deficiency throughout the study (0% vs 3.8%, P=1.0). The increases in the folate deficiency were only discovered in the female group (3.7% vs 20%, P=0.031) and the obese without type 2 diabetes (T2D) group after LSG (27.3% vs 47.1%, P=0.031). A decrease in the ferritin levels and an increase in iron deficiency at 12 months post-surgery were found among all patients. Conclusion Based on 12 months of follow-up, LSG is effective in controlling metabolic syndrome and has a modest effect on nutritional deficiencies, which suggests that LSG is an effective and comparably safe procedure for Chinese patients considering nutritional deficiencies at 12 months post-surgery.
Recycling Minerals in Microalgae Cultivation through a Combined Flash Hydrolysis–Precipitation Process

The high demand of nutrients is one of the major limitations to a sustainable large-scale production of microalgae-derived biofuels. This issue is particularly critical for phosphorus, whose natural reserves will soon be depleted. This work aims at testing the possibility of recycling phosphorus from the microalgal biomass in the form of magnesium ammonium phosphate (MAP, or struvite), a stable fertilizer suitable for transportation and long-term storage purposes, obtained through flash hydrolysis (FH) of microalgae followed by precipitation of the extracted minerals. To this goal, microalgae growth experiments were carried out with Scenedesmus sp. , in batch and continuous lab-scale photobioreactors, using struvite recovered from the proposed process, thus replacing traditional phosphate fertilizers in the cultivation medium. Results showed that the growth rate and productivity obtained with the recycled minerals as phosphorus source equal those achieved in the standard medium, suggesting that the propose.
Antioxidant, Antiproliferative Properties and Chemical Composition of the Ethanolic Extract from Leaves and Stems of Lebanese Anacyclus nigellifolius Boiss

Objective: The present study aimed to determine the content of the ethanolic extract from leaves and stems of fresh Lebanese Anacyclus nigellifolius Boiss in primary and secondary metabolites and to evaluate its antioxidant and anti-tumor activities on Human lung A549 adenocarcinoma cells. Also, the effect of low doses of chemotherapeutic agent the Cisplatin (CDDP) combined with A. nigellifolius on these cell lines has been studied. Methods: Phytochemical screening of the extract was done using standard classical tests. Total phenolic and total flavonoid contents have been determined using the Folin–Ciocalteau reagent Original Research Article Hijazi et al.; JAMPS, 11(4): 1-9, 2016; Article no.JAMPS. 31197 2 and aluminum chloride method respectively. Also, different classic methods have been used to quantify alkaloids, lipids and ash. On the other hand the antioxidant ability has been estimated using 1, 1-diphenyl-2-picrylhydrazyl (DPPH) method. In addition, the anti-tumor activity of the extract has been evaluated using Neutral Red assay. Results: The obtained results of the phytochemical screening showed the existence of terpenoids, flavonoids, phenols, saponin, glycosides and tannins in ethanol extract. In addition, this extract has showed higher contents in both total phenolic and total flavonoids. On the other hand, the DPPH method showed strong scavenging activity with an IC50=0.178 mg/mL. Moreover, an antiproliferative activity was observed by inhibiting the viability of A549 cell line in a dosedependent manner. Conclusion: All these results indicated that A. nigellifolius might be used in the prevention from some diseases related to the oxidative stress.