Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous Quantification Of Ofloxacin And Ornidazole From Combined Pharmaceutical Drug Formulation By Hplc.

In present study, a successful attempt has been made to develop a simple, sensitive and validated HPLC method for the simultaneous determination of ofloxacin and ornidazole from combined pharmaceutical drug formulation. Chromatographic separation of ofloxacin and ornidazole was achieved on C-18 column Water: ACN: TEA at a wavelength 230 nm. The method was validated in the terms of its linearity, accuracy, precision, robustness, ruggedness, LOD and LOQ. Linearity of the method was found to be in the concentration range of 4-40 μg/mL for ofloxacin and 10-100 μg/mL for ornidazole with correlation coefficient greater than 0.999 for both the analytes. The total eluting time for the both components is less than eight minutes. Proposed method was found to be simple, precise, and accurate and can be successfully applied for routine quality control analysis and simultaneous determination of ofloxacin and ornidazole in combined pharmaceutical drug formulations. P. V. REGE Department Of Chemistry, Ramnarain Ruia College, Matunga (E), Mumbai-19
A Riesz representation theorem for log-concave functions

The classic Riesz representation theorem characterizes all linear and increasing functionals on the space $C_{c}(X)$ of continuous compactly supported functions. A geometric version of this result, which characterizes all linear increasing functionals on the set of convex bodies in $\mathbb{R}^{n}$, was essentially known to Alexandrov. This was used by Alexandrov to prove the existence of mixed area measures in convex geometry. In this paper we characterize linear and increasing functionals on the class of log-concave functions on $\mathbb{R}^{n}$. Here"linear"means linear with respect to the natural addition on log-concave functions which is the sup-convolution. Equivalently, we characterize pointwise-linear and increasing functionals on the class of convex functions. For some choices of the exact class of functions we prove that there are no non-trivial such functionals. For another choice we obtain the expected analogue of the result for convex bodies. And most interestingly, for yet another choice we find a new unexpected family of such functionals. Finally, we explain the connection between our results and recent work done in convex geometry regarding the surface area measure of a log-concave functions. An application of our results in this direction is also given.
Conformal histogram regression

This paper develops a conformal method to compute prediction intervals for non-parametric regression that can automatically adapt to skewed data. Leveraging black-box machine learning algorithms to estimate the conditional distribution of the outcome using histograms, it translates their output into the shortest prediction intervals with approximate conditional coverage. The resulting prediction intervals provably have marginal coverage in ﬁnite samples, while asymptotically achieving conditional coverage and optimal length if the black-box model is consistent. Numerical experiments with simulated and real data demonstrate improved performance compared to state-of-the-art alternatives, including conformalized quantile regression and other distributional conformal prediction approaches
Free Energy Node Embedding via Generalized Skip-Gram With Negative Sampling

A widely established set of unsupervised node embedding methods can be interpreted as consisting of two distinctive steps: i) the definition of a similarity matrix based on the graph of interest followed by ii) an explicit or implicit factorization of such matrix. Inspired by this viewpoint, we propose improvements in both steps of the framework. On the one hand, we propose to encode node similarities based on the free energy distance, which interpolates between the shortest path and the commute time distances, thus, providing an additional degree of flexibility. On the other hand, we propose a matrix factorization method based on a loss function that generalizes that of the skip-gram model with negative sampling to arbitrary similarity matrices. Compared with factorizations based on the widely used <inline-formula><tex-math notation="LaTeX">$\ell _{2}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>ℓ</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="zhu-ieq1-3206175.gif"/></alternatives></inline-formula> loss, the proposed method can better preserve node pairs associated with higher similarity scores. Moreover, it can be easily implemented using advanced automatic differentiation toolkits and computed efficiently by leveraging GPU resources. Node clustering, node classification, and link prediction experiments on real-world datasets demonstrate the effectiveness of incorporating free-energy-based similarities as well as the proposed matrix factorization compared with state-of-the-art alternatives.
On the Secrecy Capacity of 2-user Gaussian Z-Interference Channel with Shared Key

In this paper, the role of secret key with finite rate is studied to enhance the secrecy performance of the system when users are operating in interference limited scenarios. To address this problem, a 2-user Gaussian Z-IC with secrecy constraint at the receiver is considered. One of the fundamental problems here is how to use the secret key as a part of the encoding process. The paper proposes novel achievable schemes, where the schemes differ from each other based on how the key has been used in the encoding process. The first achievable scheme uses one part of the key for one-time pad and remaining part of the key for wiretap coding. The encoding is performed such that the receiver experiencing interference can decode some part of the interference without violating the secrecy constraint. As a special case of the derived result, one can obtain the secrecy rate region when the key is completely used for one-time pad or part of the wiretap coding. The second scheme uses the shared key to encrypt the message using one-time pad and in contrast to the previous case no interference is decoded at the receiver. The paper also derives an outer bound on the sum rate and secrecy rate of the transmitter which causes interference. The main novelty of deriving outer bound lies in the selection of side information provided to the receiver and using the secrecy constraint at the receiver. The derived outer bounds are found to be tight depending on the channel conditions and rate of the key. The scaling behaviour of key rate is also explored for different schemes using the notion of secure GDOF. The optimality of different schemes is characterized for some specific cases. The developed results show the importance of key rate splitting in enhancing the secrecy performance of the system when users are operating under interference limited environment.
TableZa - A classical Computer Vision approach to Tabular Extraction

Computer aided Tabular Data Extraction has always been a very challenging and error prone task because it demands both Spectral and Spatial Sanity of data. In this paper we discuss an approach for Tabular Data Extraction in the realm of document comprehension. Given the different kinds of the Tabular formats that are often found across various documents, we discuss a novel approach using Computer Vision for extraction of tabular data from images or vector pdf(s) converted to image(s).
Teaching the incompressible Navier–Stokes equations to fast neural surrogate models in three dimensions

Physically plausible fluid simulations play an important role in modern computer graphics and engineering. However, in order to achieve real-time performance, computational speed needs to be traded-off with physical accuracy. Surrogate fluid models based on neural networks (NN) have the potential to achieve both, fast fluid simulations and high physical accuracy. However, these approaches rely on massive amounts of training data, require complex pipelines for training and inference or do not generalize to new fluid domains. In this work, we present significant extensions to a recently proposed deep learning framework, which addresses the aforementioned challenges in 2D. We go from 2D to 3D and propose an efficient architecture to cope with the high demands of 3D grids in terms of memory and computational complexity. Furthermore, we condition the neural fluid model on additional information about the fluid’s viscosity and density which allows simulating laminar as well as turbulent flows based on the same surrogate model. Our method allows to train fluid models without requiring fluid simulation data beforehand. Inference is fast and simple, as the fluid model directly maps a fluid state and boundary conditions at a moment t to a subsequent fluid state at t+ dt. We obtain real-time fluid simulations on a 128x64x64 grid that include various fluid phenomena such as the Magnus effect or Kármán vortex streets and generalize to domain geometries not considered during training. Our method indicates strong improvements in terms of accuracy, speed and generalization capabilities over current 3D NN-based fluid models.
Effective one-body multipolar waveform model for spin-aligned, quasicircular, eccentric, hyperbolic black hole binaries

Building upon recent work, we present an improved effective-one-body (EOB) model for spin-aligned, coalescing, black hole binaries with generic orbital configurations, i.e. quasi-circular, eccentric or hyperbolic orbits. The model relies on the idea of incorporating general Newtonian prefactors, instead of the usual quasi-circular ones, in both radiation reaction and waveform. The major advance with respect to previous work is that the quasi-circular limit of the model is now correctly informed by numerical relativity (NR) quasi-circular simulation. This provides EOB/NR unfaithfulness for the dominant quadrupolar waveform, calculated with Advanced LIGO noise, at most of the order of $1\%$ over a meaningful portion of the quasi-circular NR simulations calculated by the Simulating eXtreme Spacetime (SXS) collaboration. In the presence of eccentricity, the model is similarly NR-faithful, $\lesssim 1\%$, all over the 28 public SXS NR datasets, with initial eccentricity up to $\simeq 0.2$ , mass ratio up to $q=3$ and dimensionless spin magnitudes as large as $+0.7$. Higher multipoles, up to $\ell=5$ are also reliably modeled through the eccentric inspiral, plunge merger and ringdown. For hyperbolic-like configurations, we also show that the EOB computed scattering angle is in excellent agreement with all currently available NR results.
Surface phonons limit heat conduction in thin films

Understanding microscopic heat conduction in thin ﬁlms is important for nano/micro heat transfer and thermal management for advanced electronics. As the thickness of thin ﬁlms is comparable to or shorter than a phonon wavelength, phonon dispersion relations and transport properties are signiﬁcantly modulated, which should be taken into account for heat conduction in thin ﬁlms. Although phonon conﬁnement and depletion eﬀects have been considered, it should be emphasized that surface-localized phonons (surface phonons) arise whose inﬂuence on heat conduction may not be negligible due to the high surface-to-volume ratio. However, the role of surface phonons in heat conduction has received little attention thus far. In the present work, we performed anharmonic lattice dynamics calculations to investigate the thickness and temperature dependence of in-plane thermal conductivity of silicon thin ﬁlms with sub-10-nm thickness in terms of surface phonons. Through systematic analysis of the inﬂuences of surface phonons, we found that anharmonic coupling between surface and internal phonons localized in thin ﬁlms signiﬁcantly suppresses overall in-plane heat conduction in thin ﬁlms. We also discovered that speciﬁc low-frequency surface phonons signiﬁcantly contribute to surface–internal phonon scattering and heat conduction suppression. Our ﬁndings are beneﬁcial for the thermal management of electronics and phononic devices and may lead to surface phonon engineering for thermal conductivity control.
Ignored evident multiplicity harms replicability -- adjusting for it offers a remedy

It is a central dogma in science that a result of a study should be replicable. Only 90 of the 190 replications attempts were successful. We attribute a substantial part of the problem to selective inference evident in the paper, which is the practice of selecting some of the results from the many. 100 papers in the Reproducibility Project in Psychology were analyzed. It was evident that the reporting of many results is common (77.7 per paper on average). It was further found that the selection from those multiple results is not adjusted for. We propose to account for selection using the hierarchical false discovery rate (FDR) controlling procedure TreeBH of Bogomolov et al. (2020), which exploits hierarchical structures to gain power. Results that were statistically significant after adjustment were 97% of the replicable results (31 of 32). Additionally, only 1 of the 21 non-significant results after adjustment was replicated. Given the easy deployment of adjustment tools and the minor loss of power involved, we argue that addressing multiplicity is an essential missing component in experimental psychology. It should become a required component in the arsenal of replicability enhancing methodologies in the field.
Segmentation of MRI head anatomy using deep volumetric networks and multiple spatial priors

Abstract. Purpose: Conventional automated segmentation of the head anatomy in magnetic resonance images distinguishes different brain and nonbrain tissues based on image intensities and prior tissue probability maps (TPMs). This works well for normal head anatomies but fails in the presence of unexpected lesions. Deep convolutional neural networks (CNNs) leverage instead spatial patterns and can learn to segment lesions but often ignore prior probabilities. Approach: We add three sources of prior information to a three-dimensional (3D) convolutional network, namely, spatial priors with a TPM, morphological priors with conditional random fields, and spatial context with a wider field-of-view at lower resolution. We train and test these networks on 3D images of 43 stroke patients and 4 healthy individuals which have been manually segmented. Results: We demonstrate the benefits of each source of prior information, and we show that the new architecture, which we call Multiprior network, improves the performance of existing segmentation software, such as SPM, FSL, and DeepMedic for abnormal anatomies. The relevance of the different priors was compared, and the TPM was found to be most beneficial. The benefit of adding a TPM is generic in that it can boost the performance of established segmentation networks such as the DeepMedic and a UNet. We also provide an out-of-sample validation and clinical application of the approach on an additional 47 patients with disorders of consciousness. We make the code and trained networks freely available. Conclusions: Biomedical images follow imaging protocols that can be leveraged as prior information into deep CNNs to improve performance. The network segmentations match human manual corrections performed in 3D and are comparable in performance to human segmentations obtained from scratch in 2D for abnormal brain anatomies.
Quantification of a panel for dry-eye protein biomarkers in tears: A comparative pilot study using standard ELISA and customized microarrays

Purpose This paper examines the tear concentration of cystatin S (CST4), calcyclin (S100A6), calgranulin A (S100A8), and matrix metalloproteinase 9 (MMP9), and the correlation between biomarker expression, clinical parameters, and disease severity in patients suffering from dry eye (DE). A comparison of the results is obtained via ELISA tests and customized antibody microarrays for protein quantification. Methods This single-center, observational study recruited 59 participants (45 DE and 14 controls). Clinical evaluation included an Ocular Surface Disease Index (OSDI) questionnaire, a tear osmolarity (OSM) test, the Schirmer test (SCH), tear breakup time (TBUT), fluorescein (FLUO) and lissamine green (LG) corneal staining, and meibomian gland evaluation (MGE). Tear concentrations of CST4, S100A6, S100A8, and MMP9 were measured using standard individual ELISA assays. The levels of CST4, S100A6, and MMP9 were also measured using customized multiplexed antibody microarrays. Correlations between variables were evaluated, and a significance level was p value <0.05. Results The quantification of tear protein biomarkers with ELISA showed that the concentration of CST4 was significantly (2.14-fold) reduced in tears of DE patients in comparison with control (CT) subjects (p < 0.001). S100A6 and S100A8 concentrations were significantly higher in the tears of DE patients (1.36- and 2.29-fold; p < 0.001 and 0.025, respectively) in comparison with CT. The MMP9 level was also higher in DE patients (5.83-fold), but not significantly (p = 0.22). The changes in CST4 and S100A6 concentrations were significantly correlated with dry eye disease (DED) severity. Quantification of CST4, S100A6, and MMP9, using antibody microarrays, confirmed the ELISA results. Similar trends were observed: 1.83-fold reduction for CST4 (p value 0.01), 8.63-fold increase for S100A6 (p value <0.001) and 9.67-fold increase for MMP9 (p value 0.94), but with higher sensitivity. The biomarker concentrations were significantly associated with the signs and symptoms related with DED. Conclusions S100A6, S100A8, and CST4 diagnostic biomarkers strongly correlate with DED clinical parameters. S100A6 and CST4 are also useful for grading DE severity. The multiplexed antibody microarray technique, used here for tear multi-marker quantification, appears more sensitive than standard ELISA tests.
Transgenerational effects on development following microplastic exposure in Drosophila melanogaster

Background Plastic pollution affects all ecosystems, and detrimental effects to animals have been reported in a growing number of studies. However, there is a paucity of evidence for effects on terrestrial animals in comparison to those in the marine realm. Methods We used the fly Drosophila melanogaster to study the effects that exposure to plastics may have on life history traits and immune response. We reared flies in four conditions: In media containing 1% virgin polyethylene, with no chemical additives; in media supplemented with 1% or 4% polyvinyl chloride, known to have a high content of added chemicals; and control flies in non-supplemented media. Plastic particle size ranged from 23–500 µm. We studied fly survival to viral infection, the length of the larval and pupal stage, sex ratios, fertility and the size of the resultant adult flies. We then performed crossings of F1 flies in non-supplemented media and looked at the life history traits of the F2. Results Flies treated with plastics in the food media showed changes in fertility and sex ratio, but showed no differences in developmental times, adult size or the capacity to fight infections in comparison with controls. However, the offspring of treated flies reared in non-supplemented food had shorter life cycles, and those coming from both polyvinyl chloride treatments were smaller than those offspring of controls.
Temporalis space infection secondary to an undiagnosed intra-oral foreign object – a case report

The penetration of foreign objects is one of the leading causes of maxillofacial infection following trauma. Failure to detect such objects at initial stages can lead to complications like abscess formation, cellulitis, or space infections. Detection is even more complicated if the patient presents to the maxillofacial center after a delay of days or weeks following trauma. Sole reliance on radiographs or CT can be inconclusive as most of these objects are radiolucent and can be difficult to detect even by the experienced radiologists. We report the case of a patient who had an unwitnessed trauma and presented to our center 7 days after the incident, with signs of buccal space infection. Failure to detect the embedded intra-oral wooden object at an earlier stage led to the propagation of infection to superficial temporal space. The management strategy and pitfalls associated with conventional imaging in detecting wooden object are discussed.
Genome wide DNA methylation landscape reveals glioblastoma’s influence on epigenetic changes in tumor infiltrating CD4+ T cells

CD4+ helper T (Th) cells play a critical role in shaping anti-tumor immunity by virtue of their ability to differentiate into multiple lineages in response to environmental cues. Various CD4+ lineages can orchestrate a broad range of effector activities during the initiation, expansion, and memory phase of endogenous anti-tumor immune response. In this clinical corelative study, we found that Glioblastoma (GBM) induces multi- and mixed-lineage immune response in the tumor microenvironment. Whole-genome bisulfite sequencing of tumor infiltrating and blood CD4+ T-cell from GBM patients showed 13571 differentially methylated regions and a distinct methylation pattern of methylation of tumor infiltrating CD4+ T-cells with significant inter-patient variability. The methylation changes also resulted in transcriptomic changes with 341 differentially expressed genes in CD4+ tumor infiltrating T-cells compared to blood. Analysis of specific genes involved in CD4+ differentiation and function revealed differential methylation status of TBX21, GATA3, RORC, FOXP3, IL10 and IFNG in tumor CD4+ T-cells. Analysis of lineage specific genes revealed differential methylation and gene expression in tumor CD4+ T-cells. Interestingly, we observed dysregulation of several ligands of T cell function genes in GBM tissue corresponding to the T-cell receptors that were dysregulated in tumor infiltrating CD4+ T-cells. Our results suggest that GBM might induce epigenetic alterations in tumor infiltrating CD4+ T-cells there by influencing anti-tumor immune response by manipulating differentiation and function of tumor infiltrating CD4+ T-cells. Thus, further research is warranted to understand the role of tumor induced epigenetic modification of tumor infiltrating T-cells to develop effective anti-GBM immunotherapy.
Psychophysical condition of adolescents in coronavirus disease 2019

This study investigated the prevalence of depression and levels of physical fitness in high school students during the coronavirus disease 2019 (COVID-19) pandemic. One hundred fifty participants were divided into four groups according to the number of days that they felt depressed due to the COVID-19 during the last 7 days: no-depression group (NDG, n=17), some-depression group (SDG, n=32), occasional-depression group (ODG, n=81), and most-depression group (MDG, n=20). Shuttle run for endurance capacity and standing long jump for muscle power were selected to estimate the states of type I and type II muscle fibers, respectively. This study found that the Center for Epidemiologic Studies Depression Scale score of MDG was significantly higher than those of other three groups. In addition, ODG and MDG showed the lowest interest in their school studies compared with other groups. Endurance capacity showed a significant difference among groups, whereas muscle power showed no significant difference among groups. In conclusion, this study suggests that higher levels of depression reflect a greater lack of interest in life and academics. Furthermore, lower endurance capacity appears to be associated with more severe levels of depression. This highlights the importance of maintaining type I muscle fibers through regular aerobic exercise.