Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti-drug Antibody Magnitude and Clinical Relevance Using Signal to Noise (S/N): Bococizumab Case Study

Historically, the biopharmaceutical industry has used titer to characterize the magnitude of an anti-drug antibody (ADA) response. While reporting levels of antibodies in terms of titer is generally understood and accepted by regulatory and medical communities, titer values are inherently variable given the multiple serial dilutions and reporting a value either directly before or interpolated at the assay cut point on the lower plateau of the assay curve range. Using S/N is an appealing alternative approach to titer as it simplifies analysis with less dilutions, significantly reducing testing, time, and resources and provides a more precise value potentially differentiating low-level ADA responses. Current bridging electrochemiluminescence (ECL) ADA assays using Meso Scale Discovery (MSD) platform are also significantly more sensitive and drug tolerant with wider assay ranges compared to historic ELISA platforms; therefore, ADA response based on S/N may help differentiate and identify those ADA samples that are more likely to be clinically relevant. Bococizumab is a humanized monoclonal antibody targeting proprotein convertase subtilisin-kexin type 9 (PCSK9), which reduces plasma levels of low-density lipoprotein (LDL) cholesterol. Bococizumab was discontinued during Phase 3 clinical development based in part on the high rate of ADA and wide variation in LDL cholesterol responses among patients. The impact of anti-bococizumab antibodies on pharmacokinetic (PK) and pharmacodynamic (PD) endpoints was originally assessed using titer. Retrospective analysis of anti-bococizumab ADA responses using S/N ratios illustrates that S/N is an acceptable alternative to titer for characterizing the magnitude of ADA response and interpretation of clinically relevant ADA.
Utilisation de la teledetection courtes longueurs d'onde et infrarouge thermique a haute resolution spatiale pour l'estimation des flux d'energie a l'echelle de la parcelle agricole

La vocation de ce travail est de cartographier les flux energetiques de surface a l'echelle de la parcelle agricole europeenne en ayant recours a la teledetection. Dans cette optique, nous nous proposons d'utiliser deux outils originaux. Le premier est le modele de bilan d'energie sebal qui estime des variables non accessibles directement par teledetection (vitesse du vent et temperature de l'air) a partir de l'information contenue dans la variabilite spatiale. Le terme variabilite spatiale signifie ici l'existence de situations hydriques contrastees, ce qui suppose que le site d'etude inclus simultanement des zones seches et humides. Le second est un jeu de donnees de teledetection vis - pir - irt a haute resolution spatiale permettant d'effectuer des cartographies a l'echelle consideree. L'utilisation conjointe de ces deux outils originaux a ete testee sur le site experimental du programme europeen reseda. L'exploitation des donnees de teledetection a permis de cartographier les trois variables d'entree du modele, i. E. L'indice de vegetation ndvi, l'albedo et la temperature de brillance de surface. Les deux premieres variables ont ete estimees a partir des mesures multidirectionnelles vis - pir par le biais de modeles parametriques de fdrb. L'obtention des cartographies de la temperature de brillance a partir des mesures irt se decomposait en trois phases. 1) une partie experimentale permettant de definir diverses caracteristiques instrumentales necessaires pour le traitement des donnees. 2) une partie corrections atmospheriques de mesures aeroportees, monocanal, multidirectionnelles et multitemporelles. 3) une partie georeferencement des images pour l'obtention de cartographies. Les mesures de terrain nous ont permis de valider le modele sur plusieurs aspects et au cours des cycles de plusieurs cultures. Nous n'avons cependant pas etes en mesure de dresser une conclusion definitive pour les flux convectifs, les estimations in-situ constituant un jeu de donnees trop peu consequent. En revanche, nous avons pu verifier que l'hypothese de sebal concernant la variabilite spatiale etait valide pour l'ensemble de l'experimentation. De meme, nous avons constate que les points originaux du modele (estimation de la vitesse du vent et de la temperature de l'air) donnaient des estimations realistes.
Impact of Moisture Content on the Dynamic Failure Energy Dissipation Characteristics of Sandstone

Rockburst frequently occurs in deep underground engineering, which poses a threat to safety and causes economic losses. Water injection into surrounding rock masses is an effective method for preventing rockburst, and the moisture content of rocks is significant for assessing the probability of rockburst. However, the majority of studies focus on the relationship between the macromechanical properties of rock masses under static loads and the moisture content of rock masses and seldom explore the impact of moisture variation (under dynamic loads) on the mechanical properties and energy dissipation. In this paper, the mechanical properties and energy dissipation of sandstone with different moisture contents have been experimentally investigated by the split Hopkinson pressure bar (SHPB) test. The test results indicate that the peak strength, dynamic elastic modulus, and unloading elastic modulus of sandstone in dry conditions are considerably larger than those in moisture conditions, and the three parameters linearly decrease as the moisture content increases from 0% to 2.58%. The distribution law of sandstone fragments with different moisture contents has been investigated by sieving test fragments with different grain sizes of grading sieves. The results show that the percentage of large grain size fragments incrementally decreases, and the percentage of small grain size fragments incrementally increases with moisture contents from 0% to 2.58%. When the moisture content ranges from 2.01%∼2.58%, the fractal dimension linearly increases, which indicates that the higher the moisture content is, the larger the dimension of the broken sandstone is. The calculation results for energy indicate that the sandstone energy attains the peak value with 0% moisture content. When the moisture content ranges from 2.01%∼2.58%, the reflected energy increases, and the transmitted energy and dissipated energy linearly decrease. In addition, the surface energy of the sandstone with different moisture contents has been investigated by converting fragments into spheres with the corresponding size. The results indicate that the smallest surface area of sandstone is obtained in dry conditions, but its surface energy in dry conditions is larger than that in moisture conditions. When the moisture ranges from 0% to 2.58%, due to 3% illite and 2% chlorite clay minerals reacting with different proportions of moisture, the surface areas of sandstone fragments linearly increase and the surface energy of sandstone linearly decreases.
The effect of energy–protein supplementation on weight, body composition and handgrip strength among pulmonary tuberculosis HIV-co-infected patients: randomised controlled trial in Mwanza, Tanzania

Undernutrition is common among smear-positive pulmonary tuberculosis (PTB+) patients. Micronutrient supplementation may improve treatment outcomes, but it is unclear whether additional energy–protein would be beneficial. The present study aimed to assess the effect of energy–protein supplementation on weight, body composition and handgrip strength against a background of high micronutrient intake during tuberculosis (TB) treatment. A total of 377 PTB+ patients co-infected with HIV were randomly allocated one or six biscuits daily for 60 d during TB treatment. Weight, arm fat area, arm muscle area and handgrip strength were assessed at baseline and 2 and 5 months. There were no effects on any outcome at 2 months, but energy–protein supplementation was associated with a 1·3 (95 % CI − 0·1, 2·8) kg marginally significant gain in handgrip strength at 5 months. However, after 2 months, energy–protein supplementation led to a weight gain of 1·9 (95 % CI 0·1, 3·7) kg among patients with cluster of differentiation 4 (CD4) counts ≥ 350 cells/μl, but not among patients with low CD4 counts ( − 0·2 kg; 95 % CI − 1·3, 0·8, Pinteraction = 0·03). Similarly, at 5 months, energy–protein supplementation led to a 2·3 (95 % CI 0·6, 4·1) kg higher handgrip strength gain among patients with CD4 counts < 350 cells/μl, but not in those with high CD4 counts (Pinteraction = 0·04). In conclusion, energy–protein supplementation to PTB+ HIV-co-infected patients had no overall effects on weight and body composition, but was associated with marginally significant gain in handgrip strength. More research is needed to develop an effective supplement, before it is recommended to TB programmes.
Spectrotemporal Structure of Receptive Fields in Areas AI and AAF of Mouse Auditory Cortex

[PDF] [Full Text] [Abstract] , October 1, 2007; 98 (4): 2182-2195. J Neurophysiol C. A. Atencio, D. T. Blake, F. Strata, S. W. Cheung, M. M. Merzenich and C. E. Schreiner Monkey Frequency-Modulation Encoding in the Primary Auditory Cortex of the Awake Owl [PDF] [Full Text] [Abstract] , January 9, 2008; 28 (2): 446-455. J. Neurosci. G. B. Christianson, M. Sahani and J. F. Linden Receptive Fields The Consequences of Response Nonlinearities for Interpretation of Spectrotemporal [PDF] [Full Text] [Abstract] , February 1, 2008; 18 (2): 301-314. Cereb Cortex P. K. Pandya, D. L. Rathbun, R. Moucha, N. D. Engineer and M. P. Kilgard Spectral and Temporal Processing in Rat Posterior Auditory Cortex [PDF] [Full Text] [Abstract] , February 20, 2008; 28 (8): 1929-1942. J. Neurosci. M. B. Ahrens, J. F. Linden and M. Sahani Multilinear Spectrotemporal Methods Nonlinearities and Contextual Influences in Auditory Cortical Responses Modeled with [PDF] [Full Text] [Abstract] , March 26, 2008; 28 (13): 3415-3426. J. Neurosci. S. Sadagopan and X. Wang Cortex Level Invariant Representation of Sounds by Populations of Neurons in Primary Auditory
Riemannian Score-Based Generative Modeling

Score-based generative models (SGMs) are a novel class of generative models demonstrating remarkable empirical performance. One uses a diﬀusion to add gradually Gaussian noise to the data, while the generative model is a “denoising” process obtained by approximating the time-reversal of this “noising” diﬀusion. However, current SGMs make the underlying assumption that the data is supported on a Euclidean manifold with ﬂat geometry. This prevents the use of these models for applications in robotics, geoscience or protein modeling which rely on distributions deﬁned on Riemannian manifolds. To overcome this issue, we introduce Riemannian Score-based Generative Models (RSGMs) which extend current SGMs to the setting of compact Riemannian manifolds. We illustrate our approach with earth and climate science data and show how RSGMs can be accelerated by solving a Schrödinger bridge problem on manifolds.
Transient Theory of Pumping Induced Depletion and Drawdown of a Stream with Finite Channel Storage

Mathematical models for groundwater pumping induced stream depletion with concomitant transient stream stage decline or drawdown are developed in this work to address a deficiency in existing models, namely, the use of a fixed-stage boundary condition or source term at the stream-aquifer interface. Existing approaches in the hydrogeology literature enforce a fixed stream stage condition during pumping by prescribing a constant-head or a general boundary condition or source term, both of which imply that the stream is an infinite water source and can replenish the aquifer indefinitely during groundwater 5 pumping. It is a major limitation of the model, as it ignores the most observable adverse effect of long-term groundwater abstraction through well in close proximity to streams, namely stage declines that can eventually lead to dry streambeds. Field data are presented to demonstrate that stream stage decline does indeed occur in response to groundwater pumping pumping near hydraulically connected streams, motivating the development of an alternative theory that predicts stream depletion with transient stream drawdown. The theory is based on the concepts of finite stream storage and 10 the mass continuity principle at the stream-aquifer interface, and is used to develop models for cases of a non-and a fully-penetrating stream. The proposed models are shown to reduce to existing fixed-stage models in the limit as stream storage becomes infinitely large, and to the confined aquifer flow with a no-flow boundary at the streambed when the stream storage vanishes. The models are then applied to field observations of both aquifer and stream drawdown, yielding estimates of aquifer hydraulic parameters as well as streambed conductance and a finite stream storage coefficient. 15 The observed and model-predicted transient drawdown behavior indicate that existing fixed-stage models (a) underestimate
Elastic scattering of electrons from the ions of argon isonuclear series

Absolute angular differential, integrated elastic and momentum transfer cross-sections for elastically scattered electrons from the ions ( Ar + − Ar 18 + ) of argon isonuclear series, over the energy range 3.3–100 eV, are calculated employing a complex electron-ion optical potential within the framework of Dirac relativistic partial wave analysis. The cross sections are analyzed in terms of the contributions from pure Coulomb filed and the short range electron-ion complex optical potential comprising static, exchange, correlation-polarization and absorption potentials. Comparison of our calculations with the available experimental data and other theoretical calculations shows a satisfactory agreement.
Observations of Solar Wind from Earth-directed Coronal Pseudostreamers

Low-speed (≲450 km s−1) solar wind is widely considered to originate from streamer loops that intermittently release their contents into the heliosphere, in contrast to high-speed wind, which has its source in large coronal holes. To account for the presence of slow wind far from the heliospheric current sheet (HCS), it has been suggested that “pseudostreamers” rooted between coronal holes of the same polarity continually undergo interchange reconnection with the adjacent open flux, producing a wide band of slow wind centered on the separatrix/plasma sheet that extends outward from the pseudostreamer cusp. Employing extreme-ultraviolet images and potential-field source-surface extrapolations, we have identified 10 Earth-directed pseudostreamers during 2013–2016. In situ measurements show wind speeds ranging from ∼320 to ∼600 km s−1 in the days immediately preceding and following the predicted pseudostreamer crossings, with the proton densities and O7+/O6+ ratios tending to be inversely correlated with the bulk speed. We also identify examples of coronal holes that straddle the solar equator and give rise to wind speeds of order 400 km s−1. Our results support the idea that the bulk of the slow wind observed more than a few degrees from the HCS originates from just inside coronal holes.
Removing Scattered Light in Biomedical Images

Scattered light is a common issue in various biomedical images such as fluorescence and X-ray images, leading to a hazy view of the imaging objects and thus degrading the image quality. To tackle this issue, this paper proposes a novel mathematical model to remove such scattered light. Our model is guaranteed to produce an image with better image contrast than the input. We also present a Local Guided Total Variation filter to solve this model. The proposed filter has linear computation complexity with respect to the total number of pixels, resulting in a high computational performance. Extensive experiments have been conducted to confirm the effectiveness of the proposed model and efficiency of the presented filter. Our method can be applied to a wide range of images to remove their scattered light. The open-source code is publicly available at http://gyh8.github. io.
Context-Gan: Controllable Context Image Generation Using Gans

We propose an enhancement to label-to-image GANs. Based on a Pix2Pix architecture, ConText-GAN allows generating images in a controlled way. Given a feature map as input, ConText-GAN can generate images with a specified layout and label content. As an application, ConText-GAN is used to perform a more realistic than usual data augmentation from an MRI dataset. We show the validity of the generated images with respect to the input feature maps. The relevance of the approach is demonstrated by the improvement of the segmentation result following a data augmentation performed with ConText-GAN compared to classical methods. A practical application is presented in the challenging context of U-Net segmentation of MRI of fat infiltrated muscles.
Development and characterization of a conditionally immortalized human fetal osteoblastic cell line

We report the establishment of a human fetal osteoblast cell line derived from biopsies obtained from a spontaneous miscarriage. Primary cultures isolated from fetal tissue were transfected with a gene coding for a temperature‐sensitive mutant (tsA58) of SV40 large T antigen along with a gene coding for neomycin (G418) resistance. Individual neomycin resistant colonies were screened for alkaline phosphatase (AP)‐specific staining. The clone with the highest AP level, hFOB 1.19, was examined further for other osteoblast phenotypic markers. Incubation of hFOB cells at the permissive temperature (33.5°C) resulted in rapid cell division, whereas little or no cell division occurred at the restrictive temperature (39.5°C). Both AP activity and osteocalcin (OC) secretion increased in a dose‐dependent manner following dihydroxyvitamin D3 (1,25‐D3) treatment when cultured at either temperature. However, AP and 1,25‐D3‐induced OC levels were elevated in confluent hFOB cells cultured at 39.5°C compared with 33.5°C. Treatment of hFOB cells with 1–34 parathyroid hormone (PTH) resulted in an increase in cAMP levels. Upon reaching confluence, hFOB cultures went through programmed differentiation and formed mineralized nodules as observed by von Kossa staining. Further, immunostaining of postconfluent, differentiated hFOB cells showed that high levels of osteopontin, osteonectin, bone sialoprotein, and type I collagen were expressed. Therefore, the clonal cell line hFOB 1.19 provides a homogeneous, rapidly proliferating model system to study certain stages of human osteoblast differentiation.
Hyperspectral Image Classification of Agricultural Tillage Practices Using Spatial-aware Collaborative Representation

Classification of conservation tillage practices from hyperspectral imagery is challenging due to spectral similarity between soils and senescent crop residues. Using both spectral and spatial information can greatly improve the classification accuracy. In this paper, we investigate the collaborative representation classifiers in tillage mapping due to their highly efficient performance for large-scale image classification, which is because such classifiers do not require a training phase. To better utilize the spatial information in addition to the spectral information in a hyperspectral image, we focus on a spatial-aware collaborative representation classifier, which can directly incorporate the spatial information during classification by adding a spatial regularization term to the objective function. Experimental results demonstrate its higher accuracy and lower computational cost compared to other types of classifiers.
Multiple Instance Learning for Lymph Node Metastasis Prediction from Cervical Cancer MRI

Lymph node metastasis (LNM) is an important prognostic factor for recurrence and overall survival of cancer patients. The current LNM diagnosis is based on histopathologic examination after surgical lymphadenectomy, but an accurate and noninvasive method for LNM diagnosis is essential in selecting reasonable surgical operations and treatment plans. This paper presents an attention based multiple instance learning (MIL) model to diagnose LNM from cervical cancer multimodal MRI. The proposed MIL model adopts convolutional neural network (CNN) to extract features from multimodal MRI and attention-based pooling to make patient-level LNM status prediction. By incorporating the MIL and attention mechanism, the top rank MRI slice with informative regions in each LNM positive patient is visualized to provide the interpretability for LNM diagnosis. Experiments evaluated on a cohort of 241 cervical cancer patients show improvements in LNM status prediction compared with existing comparative models, which indicates the advantages of our designed model.
ReCPos: Deep Learning Network for 5G NR High-precision Positioning

With the increasing demand for location-based services, various positioning technologies have been proposed. However, the existing positioning technologies are often limited to the line-of-sight (LOS) scenario and incapable of providing accurate results under non-LoS (NLOS) scenario. Therefore, we propose ReCPos net, a deep residual convolutional neural network for high-precision positioning in heavy NLOS scenario, where four residual modules with distinct structures are designed. After the preprocessing for the channel impulse response (CIR) and reference signal received power (RSRP), the designed residual modules are adopted to extract the high-dimensional feature vector, then the location can be predicted by the ReCPos net. The experiment results indicate that the proposed ReCPos could reduce the positioning error by at least 20.0% compared to the existing AI-based positioning schemes in heavy NLOS conditions with lower model complexity and computing power. The input of CIR combined with RSRP and truncating CIR outperforms that of CIR alone in terms of positioning accuracy.
Detection of Powdery Mildew Pest in Apple Tree Leaves Using Deep Learning in Intelligent Sprayer Robots

Early detection of plant pests and diseases is crucial in agriculture. Deep learning can be utilized to more accurately diagnose diseases and pests on leaves and other parts of the crop. This study aims to use deep learning and processing of leaf images to identify powdery mildew in apple trees. It proposes a novel method using a transfer learning algorithm that uses minimal input images in 2 classes of powdery mildew and healthy to identify this type of pest with high accuracy. This method is evaluated by applying it to three various convolutional neural network (CNN) architectures, including VGG16 [1], AlexNet [2], and GoogLeNet [3]. The results show that the error resulting from the effect of angle and intensity of illumination in real field images can be effectively removed by this method and delineate that the AlexNet exhibits a higher accuracy of 99.53%.
Minimizing Energy Loss for Optimum Storage Capacity Design in Distributed Power Flow System

The design of a distributed power flow system (DPFS) is vital to focus on how to optimally assign the intermittent renewable energy (RE) resources to the unpredictable power loads with the integration of energy storage system (ESS). This is because the renewable energy resources such as solar photovoltaic, fuel cell, and so on are strongly depending on the environmental changes and the power loads are tightly operating according to the unplanned human activities. Thus, it is a highly significant challenge to design the energy storage system efficiently in terms of minimum energy loss and optimum storage capacity before the deployment phase. In this paper, the DPFS that incorporates an optimization approach has been investigated with real experimental dataset of smart home environment by considering two power storages and two home appliances based on human daily activities in both summer and winter seasons. Numerical simulation results reveal that the energy loss and the optimum storage capacity can be reduced greatly in winter when the load shifting algorithm is considered in the presence of power fluctuations from both power generations and power loads.