Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep learning with aerial surveys for extensive livestock hotspot recognition in the Brazilian Semi-arid Region

ABSTRACT In the Brazilian Semi-arid Region, extensive livestock farming with ecoproductive management is the most efficient way to maintain and increase the production of goat products (e.g., meat) with of not depleting environmental resources. This set of actions (induced goat migration and pasture closure) is part of Livestock 4.0, in which Industry 4.0 feed areas are efficiently managed using artificial intelligence and deep learning properly monitored by the producer and the consumer. The objective of this work was to identify pasture areas with Opuntia ficus-indica (Mill, Cactaceae) forage palm species for breeding and production of Capra aegagrus-hircus goats (Lineu, Bovidae) using aerial survey images captured by drones classified using deep learning techniques. The methodological steps of the Industry Architecture Reference Model 4.0 were adapted to the field situation (Semi-arid Region) including (A) study area delimitation, (B) image collection (by drones), (C) deep learning training, convolutional neural network (CNN) training, (D) training accuracy analysis, and (E) automatic goat production evaluation and validation. The area classification based on the forage palm density allowed us to measure the environmental degradation caused by livestock. Stimulated goat migration reduced this degradation as well as increased goat biomass and volume production.
Sensory Evaluation of Interference Suppression in Odor Reproduction Using Mass Spectrometry

Odor sensing enables us to sense odorant stimuli. This sensation causes a deep subconscious response in humans in various ways and represents an indispensable sensation in daily life. However, unwanted compounds called fixatives, which can cause a contradiction in odorant analysis especially between mass spectrometry and human subject, are included in odorant samples. Moreover, we do not know the pure odorant mass spectrum. Therefore, it is essential to eliminate the interference of fixatives from the odor sample mass spectrum data and to extract the pure odor mass spectrum. In the present study, we performed independent component analysis (ICA) on the mass spectra of odor samples to remove the influence of fixatives. The advantage of the ICA in separating independent components without a priori knowledge of the original data is useful. The abundance of essential oil mass spectra that we gathered were utilized as odorant samples. The results were compared with sensory test data from a human subject for a better study of fixative influence. It was revealed that ICA could extract the pure odor sample mass spectrum data without the influence of fixatives, even if the fixatives were added to the odor samples. This study’s outcomes allow us to analyze more odor samples for odorant analysis, not hindered by the influence of unwanted compounds.
SARS-CoV-2 epitope-specific T cells: Immunity response feature, TCR repertoire characteristics and cross-reactivity

The devastating COVID-19 pandemic caused by SARS-CoV-2 and multiple variants or subvariants remains an ongoing global challenge. SARS-CoV-2-specific T cell responses play a critical role in early virus clearance, disease severity control, limiting the viral transmission and underpinning COVID-19 vaccine efficacy. Studies estimated broad and robust T cell responses in each individual recognized at least 30 to 40 SARS-CoV-2 antigen epitopes and associated with COVID-19 clinical outcome. Several key immunodominant viral proteome epitopes, including S protein- and non-S protein-derived epitopes, may primarily induce potent and long-lasting antiviral protective effects. In this review, we summarized the immune response features of immunodominant epitope-specific T cells targeting different SRAS-CoV-2 proteome structures after infection and vaccination, including abundance, magnitude, frequency, phenotypic features and response kinetics. Further, we analyzed the epitopes immunodominance hierarchy in combination with multiple epitope-specific T cell attributes and TCR repertoires characteristics, and discussed the significant implications of cross-reactive T cells toward HCoVs, SRAS-CoV-2 and variants of concern, especially Omicron. This review may be essential for mapping the landscape of T cell responses toward SARS-CoV-2 and optimizing the current vaccine strategy.
Deep Learning for Parkinson's Disease Diagnosis: A Short Survey

Parkinson’s disease (PD) is a serious movement disorder that may eventually progress to mild cognitive dysfunction (MCI) and dementia. According to the Parkinson’s foundation, one million Americans were diagnosed with PD and almost 10 million individuals suffer from the disease worldwide. An early and precise clinical diagnosis of PD will ensure an early initiation of effective therapeutic treatments, which will potentially slow down the progression of the disease and improve the quality of life for patients and their caregivers. Machine and deep learning are promising technologies that may assist and support clinicians in providing an objective and reliable diagnosis of the disease based upon significant and unique features identified from relevant medical data. In this paper, the author provides a comprehensive review of the artificial intelligence techniques that were recently proposed during the period from 2016 to 2022 for the screening and staging of PD as well as the identification of the biomarkers of the disease based on Electroencephalography (EEG), Magnetic Resonance Imaging (MRI), speech tests, handwriting exams, and sensory data. In addition, the author highlights the current and future trends for PD diagnosis based machine and deep learning and discusses the limitations, challenges, potential future solutions, and recommendations for a reliable application of machine and deep learning for PD detection and screening.
Response of microbial community of surface and deep chlorophyll maximum to nutrients and light in South China Sea

In oligotrophic seas such as South China Sea, the subsurface or deep chlorophyll maximum (DCM) is always present. The surface planktonic community receives sufficient light, but is short of nutrients. The DCM layer is under light limitation, but frequent supply of nutrients. Therefore, vertical mixing becomes critical in determining their community composition and drives their changes by responding to light and nutrients. In this study, we conducted an onboard experiment by collecting seawater samples at surface and the DCM layer and adding nutrients, and incubated them under full sunlight and 10% light, and examined the diversity of bacterial and eukaryotic communities and their cell abundance using 16/18S high throughput sequencing and FCM approaches. Our study found large differences in bacterial and eukaryotic community structure and cell abundance between the surface and DCM. After 72 hours of culture, taxonomically the incubated surface water was dominated by pico-eukaryotic phytoplankton, while the incubated DCM layer water is dominated by diatoms, which suggests diatoms are the main functional group of phytoplankton bloom after a vertical mixing event. These findings indicate that phytoplankton at the DCM respond to enhanced light and frequent supplied nutrients due to vertical mixing and thus maintain primary productivity in the otherwise oligotrophic oceans.
Integrated bioinformatics analysis identifies shared immune changes between ischemic stroke and COVID 19

Although COVID-19 is primarily a respiratory disease, its neurological complications, such as ischemic stroke (IS), have aroused growing concerns and reports. However, the molecular mechanisms that underlie IS and COVID-19 are not well understood. Therefore, we implemented transcriptomic analysis from eight GEO datasets consist of 1191 samples to detect common pathways and molecular biomarkers in IS and COVID-19 that help understand the linkage between them. Differentially expressed genes (DEGs) were detected for IS and COVID-19 separately for finding shared mechanisms and we found that immune-related pathways were outlined with statistical significance. JAK2, which was identified as a hub gene, was supposed to be a potential therapeutic gene targets during the immunological process of COVID-19 and IS. Besides, we found a decrease in the proportion of CD8+ T and T helper 2 cells in the peripheral circulation of both COVID and IS patients, and NCR3 expression was significantly correlated with this change. In conclusion, we demonstrated that transcriptomic analyses reported in this study could make a deeper understanding of the common mechanism and might be promising for effective therapeutic for IS and COVID-19.
Optimal control of random walks, birth and death processes, and queues

This is a study of simple random walks, birth and death processes, and M/M/s queues that have transition probabilities and rates that are sequentially controlled at jump times of the processes. Each control action yields a one-step reward depending on the chosen probabilities or transition rates and the state of the process. The aim is to find control policies that maximize the total discounted or average reward. Conditions are given for these processes to have certain natural monotone optimal policies. Under such a policy for the M/M/s queue, for example, the service and arrival rates are non-decreasing and non-increasing functions, respectively, of the queue length. Properties of these policies and a linear program for computing them are also discussed.
Quantum size effects in metal particles

Influence des effets dimensionnels quantiques sur le comportement electronique des particules metalliques. Etude de la distribution des espacements entre les niveaux d'energie. Application de la theorie de Kubo. Discussion des resultats experimentaux de la litterature, en particulier du deplacement de Knight
Peripartum Pubic Symphysis Diastasis

Importance Peripartum separation of the pubic symphysis is a rare but potentially severe complication of childbirth, which may lead to prolonged immobilization. Thus, prompt diagnosis and treatment are paramount. Objective The purpose of this review is to define peripartum separation of the pubic symphysis and provide a thorough review of its etiology, clinical manifestations, diagnostic imaging techniques, management, and prognosis. Evidence Acquisition This was a literature review using PubMed and Google Scholar. Results Peripartum pubic symphysis separation is defined as disruption of the pubic symphysis joint and ligamentous structures with greater than 1 cm of separation during delivery. Risk factors include fetal macrosomia, nulliparity, and precipitous labor. Patients often present with a sensation of something “giving way” in the pubic symphysis area at the time of delivery, or with severe pain in the pubic symphysis region with attempted mobilization postpartum. In severe cases, associated hematomas, pelvic fractures, sacroiliac joint disruption, and urinary tract injury may be seen. Imaging such as x-ray or ultrasound may be used to confirm the diagnosis. Although most patients recover well with conservative management, orthopedic surgical intervention may be indicated in more severe or unresolved cases. Conclusions and Relevance Pubic symphysis separation is increasingly identified peripartum due enhanced accessibility and utilization of imaging modalities. It can be debilitating and lead to prolonged immobility postpartum. Therefore, early recognition and diagnosis are important, as this can guide decision-making for management. A multidisciplinary team approach, including coordination with obstetrics, orthopedic surgery, physical therapy, and occupational therapy should be used for early detection and treatment to ensure optimal patient outcomes. Target Audience Obstetricians and gynecologists, family physicians. Learning Objectives After completing this activity, learners should be better able to define peripartum separation of the pubic symphysis and discuss the relevant anatomy involved; identify risk factors associated with peripartum separation of the pubic symphysis; describe the typical presentation of a patient with separation of the pubic symphysis and next steps in making the diagnosis; and explain both conservative and orthopedic surgical intervention methods for treatment of peripartum separation of the pubic symphysis.
The Surgical Management of Lung Neuroendocrine Neoplasms

Simple Summary Lung neuroendocrine neoplasms comprise a diverse group of cancers that arise from pulmonary neuroendocrine cells. They account for about 20% of all primary lung cancers and classify into four subtypes: typical carcinoid, atypical carcinoid, large cell neuroendocrine carcinoma, and small cell lung carcinoma. These subtypes share some morphological and protein expression immunohistochemistry features but differ greatly in their biological behaviors. Typical and atypical carcinoids are well differentiated low- and intermediate-grade tumors, respectively, whereas large cell neuroendocrine carcinoma and small cell lung carcinoma are poorly differentiated high-grade tumors. The common thread in all lung neuroendocrine neoplasms is that patients with localized disease primarily undergo surgery, while patients with locally advanced or metastatic disease receive tailored oncological therapies. Abstract This review summarizes key recent developments relevant to the surgical management of lung neuroendocrine neoplasms (L-NENs), including typical and atypical carcinoids, large cell neuroendocrine carcinoma, and small cell lung carcinoma. This review includes recent insights into the classification, clinical presentation, diagnostic workup, treatment options, and follow-up. Highlighted topics include general principles of surgery in localized or locally advanced or metastatic L-NENs, lung-sparing surgery for small, peripheral typical carcinoids, adjuvant and systemic therapies for typical and atypical carcinoids, and surgery and adjuvant therapies for large cell neuroendocrine carcinoma and small cell lung carcinoma.
Almond cultivar identification using machine learning classifiers applied to UAV-based multispectral data

ABSTRACT In Portugal, almonds are a very important crop, due to their nutritional properties. In the northeastern part of the country, the almond sector has endured over time, with strong cultural traditions and key economic significance. In these areas, several cultivars are used. In effect, the presence of various almond cultivars implies differentiated management in irrigation, disease control, pruning system, and harvest planning. Therefore, cultivar classification is essential over large agricultural areas. Over the last decades, remote-sensing data have led to important breakthroughs in the classification of different cultivars for several crops. Nonetheless, for almonds, studies are incipient. Thus, this study aims to fill this knowledge gap and explore the classification of almond cultivars in an almond orchard. High-resolution multispectral data were acquired by an unmanned aerial vehicle (UAV). Vegetation indices (VIs) and tree structural parameters were, subsequently, estimated. To obtain an accurate cultivar identification, four machine learning classifiers, such as K-nearest neighbour (kNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were applied and optimized through the fine-tuning process. The accuracy of machine learning classifiers was analysed. SVM and RF performed best with OAs of 76% and 74% using VIs and spectral bands (GREEN, GRVI, GN, REN, ClRE). Adding the canopy height model (CHM) improved performance, with RF and XGBoost having OAs of 88% and 84%. kNN performed worst with an OA of 73% using only VIs and spectral bands, 80% with VIs, spectral bands and CHM, and 93% with VIs, CHM, and tree crown area (TCA). The best performance was achieved by RF and XGBoost with OAs of 99% using VIs, CHM, and TCA. These results demonstrate the importance of the feature selection process. Moreover, this study reveals the feasibility of remote-sensing data and machine learning classifiers in the classification of almond cultivars.
Departure from Paschen's Law of Breakdown in Gases

The failure of Paschen's law of electrical breakdown in gases at both high pressures and extremely small electrode separations is explained by a single breakdown mechanism. The breakdown field in each of these cases is sufficiently great to draw measurable field-emission current from the cathode, which produces a relatively small number of ions. The space-charge field of these ions is great enough to increase the field-emission current appreciably even when the ratio of ion current to electron current is less than one percent. As more ionic space charge is produced, each ion becomes more effective in enhancing the electron current until the breakdown condition is attained. An expression is derived for the yield of electrons per positive ion as a function of the applied field. This expression is shown to be in quantitative agreement with values of $\ensuremath{\gamma}$ derived from published data on breakdown voltages at high pressures. It is shown also that the same process explains breakdown at extremely small separations below the "minimum sparking potential. "
Testing the independence of variables for specific covariance structures: A simulation study

In this work, we show how it is possible to test the nullity of covariances, in a set of variables, using a simple univariate procedure. The methodology proposed enables us to perform the multivariate test of independence of several variables, under specific conditions for the covariance structure. The methodology proposed may be used in the high‐dimensional setting and, given its simplicity, allows to overcome the difficulties in using the exact distribution of the statistic used in the likelihood ratio testing procedure. A simulation study is provided to assess the power and significance level, in different scenarios, of the testing procedure proposed when compared with different likelihood ratio tests and a methodology available in the literature.
Higher-order methods for large-scale optimization

There has been an increased interest in optimization for the analysis of large-scale data sets which require gigabytes or terabytes of data to be stored. A variety of applications originate from the fields of signal processing, machine learning and statistics. Seven representative applications are described below. Magnetic Resonance Imaging (MRI): A medical imaging tool used to scan the anatomy and the physiology of a body [80]. Image inpainting: A technique for reconstructing degraded parts of an image [14]. Image deblurring: Image processing tool for removing the blurriness of a photo caused by natural phenomena, such as motion [64]. Radar pulse reconstruction [120]. Genome-Wide Association study (GWA): DNA comparison between two groups of people (with/without a disease) in order to investigate factors that a disease depends on [112]. Recommendation systems: Classification of data (i.e., music or video) based on user preferences [67]. Data fitting: Sampled data are used to simulate the behaviour of observed quantities. For example estimation of global temperature based on historic data [65]. Large-scale problems impose restrictions on methods that have been so far employed. The new methods have to be memory efficient and ideally, within seconds they should offer noticeable progress towards a solution. First-order methods meet some of these requirements. They avoid matrix factorizations, they have low memory requirements, additionally, they sometimes offer fast progress in the initial stages of optimization. Unfortunately, as demonstrated by numerical experiments in this thesis, first-order methods miss essential information about the conditioning of the problems, which might result in slow practical convergence. The main advantage of first-order methods which is to rely only on simple gradient or coordinate updates becomes their essential weakness. We do not think this inherent weakness of first-order methods can be remedied. For this reason, the present thesis aims at the development and implementation of inexpensive higher-order methods for large-scale problems.
A Survey of Deep Learning Based NOMA: State of the Art, Key Aspects, Open Challenges and Future Trends

Non-Orthogonal Multiple Access (NOMA) has become a promising evolution with the emergence of fifth-generation (5G) and Beyond-5G (B5G) rollouts. The potentials of NOMA are to increase the number of users, the system’s capacity, massive connectivity, and enhance the spectrum and energy efficiency in future communication scenarios. However, the practical deployment of NOMA is hindered by the inflexibility caused by the offline design paradigm and non-unified signal processing approaches of different NOMA schemes. The recent innovations and breakthroughs in deep learning (DL) methods have paved the way to adequately address these challenges. The DL-based NOMA can break these fundamental limits of conventional NOMA in several aspects, including throughput, bit-error-rate (BER), low latency, task scheduling, resource allocation, user pairing and other better performance characteristics. This article aims to provide firsthand knowledge of the prominence of NOMA and DL and surveys several DL-enabled NOMA systems. This study emphasizes Successive Interference Cancellation (SIC), Channel State Information (CSI), impulse noise (IN), channel estimation, power allocation, resource allocation, user fairness and transceiver design, and a few other parameters as key performance indicators of NOMA systems. In addition, we outline the integration of DL-based NOMA with several emerging technologies such as intelligent reflecting surfaces (IRS), mobile edge computing (MEC), simultaneous wireless and information power transfer (SWIPT), Orthogonal Frequency Division Multiplexing (OFDM), and multiple-input and multiple-output (MIMO). This study also highlights diverse, significant technical hindrances in DL-based NOMA systems. Finally, we identify some future research directions to shed light on paramount developments needed in existing systems as a probable to invigorate further contributions for DL-based NOMA system.
Age Estimation in 0–8-Year-Old Children in France: Comparison of One Skeletal and Five Dental Methods

Age estimation in juveniles is a critical procedure in judicial cases for verification of imputability or for civil reasons when adopting children. Several methods based both on skeletal and dental growth have been performed and applied on different populations; nevertheless, few articles have compared different methods in order to test their reliability in different conditions and age ranges, and this is a clear obstacle in the creation of common guidelines for age estimation in the living. A comparison of five dental methods (Anderson, Ubelaker, Schour and Massler, Gustafson and Koch, Demirjian) and one skeletal method (Greulich a Pyle atlas) was performed on a population of 94 children aged between 0 and 8 years. Results showed that, whereas under 2 years all the methods have the same inaccuracy, over 2 years the diagram methods, such as Schour and Massler and Ubelaker’s revised one, have a lower error range than the most frequently used Greulich and Pyle atlas and Demirjian method. Schour and Massler, Gustafson and Koch, and Ubelaker methods showed, respectively, a mean error amounting to 0.40, 0.53, and 0.56 years versus the 0.74 and 0.88 years given by Demirjian and the Greulich and Pyle atlas. An in-depth analysis of the potential of several methods is necessary in order to reach a higher adherence of age estimation with the complexity of growth dynamics.