Miracle-dz/UniFAR-FTUs huggingface dataset:
DEVELOPING AN ALGORITHM FOR BODY CONDITION SCORING OF DAIRY COWS

Cow’s body condition scoring (BCS) based on neural network algorithms is necessary to monitor the health and productivity of the animals. To develop an algorithm for assessing the physiological fatness of dairy cows according to the parameters of the fermur heads, the sacrum and the hunger hollow, the authors studied eighty animals on a farm of the state unitary enterprise “Grigorievskoe”, which were divided into fatness groups (from 1 to 5). The studies were carried out in November 2021 during the morning milking. Data were collected using a 3D ToF camera O3D303. Previously, the installation of a three-dimensional camera on the farm was sim ulated, and an algorithm was developed that takes into account the cow height and the distance from the highest point of the spine to the three-dimensional camera. An algorithm for assessing the physiological fatness of dairy cows (BCS) has been developed to take into account the condition of the fermur heads, the sacrum, the hunger hollow, which determines the highest point of the withers, the proportions between the body length and width, as well as the depth of the hunger hollows and the severity of the caudal ligament. Software has been developed to register the unique number of a cow and determine the BCS, as well as show the dynamics of changes in the animal’s fatness. Data were processed in accordance with the developed algorithm. The image was processed using the regression method. Comparison results of the BCS of cows, obtained according to the developed algorithm, and the experts’ assessment showed that the algorithm error in the fatness range of 2…4 points averaged 10%. When determining the BCS of cows with borderline and limit fatness state (1 and 5 points), the measurement error by the proposed algorithm increased to 25%. Based on the results obtained, the authors recommend pre-setting a neural network for further research; determine the correction factor for fatness points 1 and 5; fi nalize the software, develop customized software and an automatic system for body condition scoring, and conduct tests.
Learning optimal integration of spatial and temporal information in noisy chemotaxis

We investigate the boundary between chemotaxis driven by spatial estimation of gradients and chemotaxis driven by temporal estimation. While it is well known that spatial chemotaxis becomes disadvantageous for small organisms at high noise levels, it is unclear whether there is a discontinuous switch of optimal strategies or a continuous transition exists. Here, we employ deep reinforcement learning to study the possible integration of spatial and temporal information in an a priori unconstrained manner. We parameterize such a combined chemotactic policy by a recurrent neural network and evaluate it using a minimal theoretical model of a chemotactic cell. By comparing with constrained variants of the policy, we show that it converges to purely temporal and spatial strategies at small and large cell sizes, respectively. We find that the transition between the regimes is continuous, with the combined strategy outperforming in the transition region both the constrained variants as well as models that explicitly integrate spatial and temporal information. Finally, by utilizing the attribution method of integrated gradients, we show that the policy relies on a non-trivial combination of spatially and temporally derived gradient information in a ratio that varies dynamically during the chemotactic trajectories.
Study of Risk Factors and Assessment of Hematotoxicity and Oxidative Stress Among Women Stroke Patient with Metabolic Syndrome

This study aims at evaluating some risk factors and the variation of certain hematological and oxidative stress markers in women with stroke disease. The observation (clinical study) was done on 130 voluntary women, divided into metabolic syndrome patients with and without stroke disease. Risk factors were estimated by Chi-square test. Results demonstrated that the high levels of family stress, self-medication and anticoagulants treatment were the most dangerous risk factors to stroke diseases, whereas brushing of teeth daily had an important protective factors against participants from disease. From biological study, results showed that RBC, hemoglobin, GSH and total thiol levels were significantly decreased (p<0.05) while WBC, neutrophile, platelet, MDA, SOD, ORAC and vit C levels were significantly increased (p<0.05) in women with metabolic syndrome (MS) + stroke patients as compared to MS patients without stroke. In conclusion, several socioeconomic and clinical factors contributed to the dispersion and development of the stroke. In addition, oxidative stress during metabolic syndrome contributes to the development of stroke disease. Dhaka Univ. J. Pharm. Sci. 21(2): 195-203, 2022 (December)
On measurement error adjustment methods in Poisson regression

This paper echoes the familiar warning that additive measurement error and multicollinearity in the explanatory variables can mislead investigators in multiple Poisson regression analysis. Often, a causal variable measured with error may be overlooked and its significance transferred to another covariate. Two measurement error adjustment methods were applied and compared in terms of mean squared error of the regression estimates as well as confidence interval coverage of the parameters. Computer simulation was used to evaluate these methods when the explanatory variables are subject to both classical and Berkson error. Results showed that the regression calibration method (RCAL) performed the best in all situations considered, except in the presence of Berkson error when the predictor variables are highly correlated. Copyright © 1999 John Wiley & Sons, Ltd.
Extracellular Vesicles from Campylobacter jejuni CDT-Treated Caco-2 Cells Inhibit Proliferation of Tumour Intestinal Caco-2 Cells and Myeloid U937 Cells: Detailing the Global Cell Response for Potential Application in Anti-Tumour Strategies

Cytolethal distending toxin (CDT) is produced by a range of Gram-negative pathogenic bacteria such as Campylobacter jejuni. CDT represents an important virulence factor that is a heterotrimeric complex composed of CdtA, CdtB, and CdtC. CdtA and CdtC constitute regulatory subunits whilst CdtB acts as the catalytic subunit exhibiting phosphatase and DNase activities, resulting in cell cycle arrest and cell death. Extracellular vesicle (EV) secretion is an evolutionarily conserved process that is present throughout all kingdoms. Mammalian EVs play important roles in regular cell-to-cell communications but can also spread pathogen- and host-derived molecules during infections to alter immune responses. Here, we demonstrate that CDT targets the endo-lysosomal compartment, partially evading lysosomal degradation and exploiting unconventional secretion (EV release), which is largely involved in bacterial infections. CDT-like effects are transferred by Caco-2 cells to uninfected heterologous U937 and homologous Caco-2 cells. The journey of EVs derived from CDT-treated Caco-2 cells is associated with both intestinal and myeloid tumour cells. EV release represents the primary route of CDT dissemination, revealing an active toxin as part of the cargo. We demonstrated that bacterial toxins could represent suitable tools in cancer therapy, highlighting both the benefits and limitations. The global cell response involves a moderate induction of apoptosis and autophagic features may play a protective role against toxin-induced cell death. EVs from CDT-treated Caco-2 cells represent reliable CDT carriers, potentially suitable in colorectal cancer treatments. Our data present a potential bacterial-related biotherapeutic supporting a multidrug anticancer protocol.
Machine Learning for Urban Air Quality Analytics: A Survey

The increasing air pollution poses an urgent global concern with far-reaching consequences, such as premature mortality and reduced crop yield, which significantly impact various aspects of our daily lives. Accurate and timely analysis of air pollution is crucial for understanding its underlying mechanisms and implementing necessary precautions to mitigate potential socio-economic losses. Traditional analytical methodologies, such as atmospheric modeling, heavily rely on domain expertise and often make simplified assumptions that may not be applicable to complex air pollution problems. In contrast, Machine Learning (ML) models are able to capture the intrinsic physical and chemical rules by automatically learning from a large amount of historical observational data, showing great promise in various air quality analytical tasks. In this article, we present a comprehensive survey of ML-based air quality analytics, following a roadmap spanning from data acquisition to pre-processing, and encompassing various analytical tasks such as pollution pattern mining, air quality inference, and forecasting. Moreover, we offer a systematic categorization and summary of existing methodologies and applications, while also providing a list of publicly available air quality datasets to ease the research in this direction. Finally, we identify several promising future research directions. This survey can serve as a valuable resource for professionals seeking suitable solutions for their specific challenges and advancing their research at the cutting edge.
Multisource Data Reconstruction-Based Deep Unsupervised Hashing for Unisource Remote Sensing Image Retrieval

Unsupervised hashing for remote sensing (RS) image retrieval first extracts image features and then uses these features to construct supervised information (e.g., pseudolabels) to train hashing networks. Existing methods usually regard RS images as natural images to extract unisource features. However, these features only contain partial information about ground objects and cannot produce reliable pseudolabels. In addition, existing methods only generate a pseudo-single-label to annotate each RS image, which cannot accurately represent multiple scenes in an RS image. To address these drawbacks, this article proposes a new Multisource data reconstruction-based deep unsupervised Hashing method, called MrHash, which explores the characteristics of RS images to construct reliable pseudolabels. In particular, we first use geographic coordinates to obtain different satellite images and develop a novel autoencoder network to extract multisource features from these images. Then, pseudo-multilabels are designed to deal with the coexistence of multiple scenes in a single image. These labels are generated by a custom probability function with extracted multisource features. Finally, we propose a novel multisemantic hash loss by using the Kullback–Leibler (KL) divergence to preserve the semantic similarity of these pseudo-multilabels in Hamming space. Our newly developed MrHash only uses multisource images to construct supervised information, and hash code generation still relies on a unisource input image. Experiments on benchmark datasets clearly show the superiority of the proposed method over state-of-the-art baselines. We have added detailed descriptions about our source code. Please check them by accessing https://github.com/sunyuxi/MrHash.
Doping-Induced Electronic and Structural Phase Transition in the Bulk Weyl Semimetal Mo1-xWxTe2

A comprehensive study of the electronic and structural phase transition from 1T` to Td in the bulk Weyl semimetal Mo1-xWxTe2 at different doping concentrations has been carried out using time-of-flight momentum microscopy (including circular and linear dichroism), X-ray photoelectron spectroscopy (XPS), X-ray photoelectron diffraction (XPD), X-ray diffraction (XRD), angle-resolved Raman spectroscopy, transport measurements, density functional theory (DFT) and Kikuchi pattern calculations. High-resolution angle-resolved photoemission spectroscopy (ARPES) at 20 K reveals surface electronic states, which are indicative of topological Fermi arcs. Their dispersion agrees with the position of Weyl points predicted by DFT calculations based on the experimental crystal structure of our samples determined by XRD. Raman spectroscopy confirms the inversion symmetry breaking for the Td -phase, which is a necessary condition for the emergence of topological states. Transport measurements show that increasing the doping concentration from 2 to 9% leads to an increase in the temperature of the phase transition from 1T` to Td from 230 K to 270 K. Magnetoresistance and longitudinal elastoresistance show significantly increased values in the Td -phase due to stimulated inter-pocket electron backscattering. The results demonstrate the close relationship between electronic properties and elastic deformations in MoTe2.
Aluminum-Doped Zinc Oxide Enzymatic Dopamine Biosensor Integrated With Potentiometric Readout Circuit Board

In this study, the standard industrial flexible printed circuit board (FPCB) was employed to fabricate the potentiometric enzymatic dopamine (DA) biosensor. The aluminum-doped zinc oxide (AZO) was used to make the sensing films of the DA sensor. This sensing film was prepared by a radio frequency (RF) sputtering system. The thickness and surface morphology of the AZO films were observed by the field emission scanning electron microscope (FE-SEM), and the elemental composition and oxidation state of the films were analyzed through X-ray electron spectroscopy (XPS). Tyrosinase (Tyr) was used as the enzyme of the sensor, which was immobilized on AZO film with (3-Aminopropyl)triethoxysilane (APTES) and glutaraldehyde. After the sensor was prepared, we integrated the sensor and the readout circuit board through the FPCB connecter and then measured the sensing characteristics of the DA sensor in the DA concentration range of $1~\mu \text{M}$ –10 mM. The sensing characteristics mostly include average sensitivity and linearity, limit of detection (LOD), interference effect, response time, reproducibility, flexibility, and lifetime. Based on the experimental results, the DA sensor in this study has good sensing characteristics. Through the enzyme, the sensor has good selectivity for DA in the presence of other relatively high concentrations interfered with biomolecules.
Improving end-to-end deep learning methods for Arabic handwriting recognition

Abstract. The number of Arabic handwriting documents has increased greatly. Studies conducted in the Arabic handwriting recognition field have progressed significantly in recent years in different areas. The existing studies for Arabic language compared to others such as Latin remains insufficient. During the last decade, neural networks (NNs) have become the de facto standard for deep learning, and the combination of two or more NNs has proved its ability to learn complex objects, such as handwriting, and emulate human brains. We proposed an approach combining convolutional NN and bidirectional long short-term memory for recognition. This unique approach makes it possible to recognize Arabic handwriting words without segmentation. The proposed architecture is very efficient in terms of accuracy for Arabic word recognition. The hyperparameters set for our model were chosen based on a structured process of randomness and grid search to increase the accuracy of the model.
Development of an in-vitro high-throughput screening system to identify modulators of genitalia development

Abstract Sexually dimorphic outgrowth and differentiation of the embryonic genital tubercles (GTs) give rise to the penis in males and the clitoris in females. Defects in androgen production or in response to androgen signaling can lead to various congenital penile anomalies in both mice and humans. Due to lack of a high-throughput screening system, identification of crucial regulators of GT sexual differentiation has been slow. To overcome this research barrier, we isolated embryonic GT mesenchymal (GTme) cells to model genitalia growth and differentiation in vitro. Using either a mechanical or fluorescence-activated cell sorting–assisted purification method, GTme cells were isolated and assayed for their proliferation using a microscopy and image analysis system, on a single cell level over time. Male and female GTme cells inherently exhibit different cellular dynamics, consistent with their in-vivo behaviors. This system allows for the rapid quantitative analyses of numerous drug treatments, and enables the discovery of potential genetic modulators of GT morphogenesis on a large scale. Using this system, we completed a 438-compound library screen and identified 82 kinase inhibitor hits. In mice, in-utero exposure to one such candidate kinase inhibitor, Cediranib, resulted in embryos with severe genitalia defects, especially in males. Gene silencing by RNAi was optimized in this system, laying the foundation for future larger-scale genetic screenings. These findings demonstrate the power of this novel high-throughput system to rapidly and successfully identify modulators of genitalia growth and differentiation, expanding the toolbox for the study of functional genomics and environmental factors.
HPLC Method Validation for the Estimation of Lignocaine HCl, Ketoprofen and Hydrocortisone: Greenness Analysis Using AGREE Score

In the current study, the reversed-phased high-pressure liquid chromatography (RP-HPLC) method was proposed for the estimation of lignocaine hydrochloride (LIG), hydrocortisone (HYD) and Ketoprofen (KET) according to International Conference for Harmonization (ICH) Q2 R1 guidelines, in a gel formulation. The chromatographic evaluation was executed using Shimadzu RP-HPLC, equipped with a C8 column and detected using UV at 254 nm wavelength, using acetonitrile and buffer (50:50) as a mobile phase and diluent, at flow rate 1 mL/min and n injection volume of 20 μL. The retention time for LIG, HYD, and KET were 1.54, 2.57, and 5.78 min, correspondingly. The resultant values of analytical recovery demonstrate accuracy and precision of the method and was found specific in identification of the drugs from dosage form and marketed products. The limit of detection (LOD) for LIG, HYD, and KET were calculated to be 0.563, 0.611, and 0.669 ppm, while the limit of quantification (LOQ) was estimated almost at 1.690, 1.833, and 0.223 ppm, respectively. The AGREE software was utilized to evaluate the greenness score of the proposed method, and it was found greener in score (0.76). This study concluded that the proposed method was simple, accurate, precise, robust, economical, reproducible, and suitable for the estimation of drugs in transdermal gels.
A Study of Customer Brand Engagement for Materialistic Groups

: Purpose: The purpose of this study is to examine how co-design activities affect consumer psychological factors, such as self-brand connection, and thus consumer-brand engagement, and to investigate the applicability of this mechanism to populations with different degrees of materialism. Design/methodology/approach: This study used a questionnaire to construct a "co-design" scenario and a materialism scale to investigate the personal characteristics of the participants, and a scenario in which consumers participated in the co-design of an apparel brand to investigate their perceived self-brand connection and customer brand engagement. Findings: Co-design activities are effective in increasing consumers' brand engagement with the brand, in which the self-brand connection plays a partially mediating role and the individual consumer's level of materialism plays a negative moderating role. Research implications: The impact of co-creation on customer psychological factors and the boundary conditions of this process should be further investigated. The results of this study will provide a reference for companies to determine whether they should engage in "value co-creation" activities with their target groups. Originality/value innovation: Current research emphasizes the importance of adopting a psychological perspective on engagement and analyzing the scope of this mechanism of action in terms of consumer characteristics.
Multi‐Year CALIPSO Observations of Ubiquitous Elevated Aerosol Layer in the Free Troposphere Over South Asia: Sources and Formation Mechanism

The vertical distribution of aerosols in the lower troposphere is critically important for assessing their impact on Earth's radiation budget and modulation of cloud microphysics. This study analyzed cloud‐free aerosol extinction coefficient (βext), aerosol subtypes, and particulate depolarization ratios obtained from CALIOP (Cloud‐Aerosol Lidar with Orthogonal Polarization) over the six regions of India during 2008–2018. We investigated unprecedented climatology of the physical and optical characteristics of elevated aerosol layers (EALs) along with their source and formation mechanism. The key findings include: (a) EALs over the Indian region were persistent between 4 and 6 km during all seasons, (b) geometrical layer thickness of EALs increased up to 36.7% and 25% from the annual mean during summer and fall seasons, respectively, compared to that of spring and winter, (c) dust and polluted dust accounted for up to 50%–80% from near‐surface to 6 km and up to 80%–90% of the EALs between 4 and 6 km, respectively for all the seasons and regions, (d) we anticipated that locally confined recirculation coupled with stratified stable layer capped within turbulent layers could be a possible mechanism of formation of stratified EALs between 4 and 6 km during winter‐spring‐fall, while in summer, vertical transport of pollutants from the PBL to mid‐troposphere due to enhanced deep convection served as a key formation mechanism of the EALs, (e) the second Modern‐Era Retrospective analysis for Research and Applications Global Modeling Initiative model reasonably simulated the shape and vertical gradient of βext with significant differences in magnitude below 4 km; however, it fails to reproduce EALs for all seasons and regions during the study period.
Deep Reinforcement Learning: Emerging Trends in Macroeconomics and Future Prospects

The application of Deep Reinforcement Learning (DRL) in economics has been an area of active research in recent years. A number of recent works have shown how deep reinforcement learning can be used to study a variety of economic problems, including optimal policy-making, game theory, and bounded rationality. In this paper, after a theoretical introduction to deep reinforcement learning and various DRL algorithms, we provide an overview of the literature on deep reinforcement learning in economics, with a focus on the main applications of deep reinforcement learning in macromodeling. Then, we analyze the potentials and limitations of deep reinforcement learning in macroeconomics and identify a number of issues that need to be addressed in order for deep reinforcement learning to be more widely used in macro modeling. RECOMMENDED CITATION: T. Atashbar and R.A. Shi 2022. “ Deep Reinforcement Learning: Emerging Trends in Macroeconomics and Future Prospects”, IMF Working Papers, WP/22/259. JEL Classification Numbers: C63, C89, D85, D87