Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Species of Galeoglossum (Orchidaceae, Cranichidinae) from Oaxaca, Mexico

Abstract Galeoglossum cactorum, a new species of Orchidaceae from the Mixteca Alta region in the state of Oaxaca, Mexico, is described and illustrated. The new species is florally similar to G. thysanochilum but differs from it in the linear-oblanceolate leaves subtended by imbricating, dark brown cataphylls, presence of a papillose thickening near the base of the labellum, proportionately shorter, obtrapezoid column with lateral laminar wings and complete fusion of the filament, and prominent apicule of the rostellum remnant. A cladistic parsimony analysis of DNA sequences of nuclear ITS region and plastid matK-trnK region showed that the three currently recognized species of Galeoglossum form a monophyletic group, which is sister to Prescottia. Galeoglossum cactorum is known from a single locality in an unusual habitat for orchids, i.e. a xerophilous scrub dominated by columnar cacti of the genus Neobuxbaumia, whereas both G. thysanochilum and G. tubulosum dwell in oak- and pine-oak forest.
A Comprehensive Review on Evolutionary Optimization Techniques Applied for Unit Commitment Problem

Unit Commitment (UC) is a key task in electric power system operation, aiming at minimizing the total cost of power generation. It is essential to monitor wide range of activities and practices of UC necessary to determine the operating plan of generating units. The UC problem is particularly crucial, when the behavior of loads at every hour interval, is oscillatory and with different operational constraints and environments. Many works have been proposed, with different optimization methods to solve the UC problem. This paper gives a detailed review of the evolutionary optimization techniques, employed for solving UC problem, by collecting them from lots of peer reviewed published papers. This review was carried out under many sections, based on various evolutionary optimization techniques, to help new researchers, dealing with modern UC problem solutions, under different situations of power system.
Experimental evidence for the attenuating effect of SOM protection on temperature sensitivity of SOM decomposition

The ability to predict C cycle responses to temperature changes depends on the accurate representation of temperature sensitivity (Q10) of soil organic matter (SOM) decomposition in C models for different C pools and soil depths. Theoretically, Q10 of SOM decomposition is determined by SOM quality and availability (referred to here as SOM protection). Here, we focus on the role of SOM protection in attenuating the intrinsic, SOM quality dependent Q10. To assess the separate effects of SOM quality and protection, we incubated topsoil and subsoil samples characterized by differences in SOM protection under optimum moisture conditions at 25 °C and 35 °C. Although lower SOM quality in the subsoil should lead to a higher Q10 according to kinetic theory, we observed a much lower overall temperature response in subsoil compared with the topsoil. Q10 values determined for respired SOM fractions of decreasing lability within the topsoil increased from 1.9 for the most labile to 3.8 for the least labile respired SOM, whereas corresponding Q10 values for the subsoil did not show this trend (Q10 between 1.4 and 0.9). These results indicate the existence of a limiting factor that attenuates the intrinsic effect of SOM quality on Q10 in the subsoil. A parallel incubation experiment of 13C‐labeled plant material added to top‐ and subsoil showed that decomposition of an unprotected C substrate of equal quality responds similarly to temperature changes in top‐ and subsoil. This further confirms that the attenuating effect on Q10 in the subsoil originates from SOM protection rather than from microbial properties or other nutrient limitations. In conclusion, we found experimental evidence that SOM protection can attenuate the intrinsic Q10 of SOM decomposition.
CMFALL: A Cascade and Parallel Multi-State Fall Detection Algorithm Using Waist-Mounted Tri-Axial Accelerometer Signals

As one of the main threats to people’s health, especially for the elderly, falls have caused a large number of accidents. Detecting falls in time can minimize the severity of the injury and save lives. Therefore, designing fall detection algorithms to assist people’s daily life has been a hot research topic. In the last decade, different fall detection approaches based on wearable sensors have been proposed. However, since the hardware resources of wearable sensors are very limited, designing accurate but energy-efficient fall detection algorithms remains an open challenge. To deal with this, an accurate but low-cost fall detection algorithm is proposed in this paper. Particularly, a novel cascade and parallel method that efficiently employs the characteristics of human falls and the advanced modeling ability of the neural network is proposed. Also, a novel design of a lightweight convolutional neural network with self-attention is proposed to achieve the best recognition/numerical complexity tradeoff. The proposed method, namely CMFALL, is evaluated together with a multitude of state-of-the-art models on a large dataset, where it performs the best with an F1-score exceeding 99%. Meanwhile, its computational cost and model size are only a few thousandths of other models. Such low computational cost and small size not only enable to embed it in a wearable sensor but also make the system power requirements quite low, which can enhance the autonomy of the wearable fall detector.
Viruses infecting bivalve molluscs

Bivalve molluscs are filter feeders and as a consequence they may bioaccumulate in their tissues viruses that infect humans and higher vertebrates. However, there have also been described mortalities of bivalve molluscs asso- ciated with viruses belonging to different families. Mass mortalities of adult Portuguese oysters, Crassostrea angulata, among French livestocks (between 1967 and 1973) were associated with irido-like virus infections. Herpesviruses were reported in the eastern oyster, Pacific oyster and European flat oyster and lately in scallops and clams. Disseminated neoplasia, a proliferative cell disorder of the circulatory system of bivalves, although of uncertain aetiology, has been suggested to be caused by retroviral infections. Other viruses described in bivalves are interpreted as members of the Papovaviridae, Togaviridae, Reoviridae, Birnaviridae and Picornaviridae. However, the lack of bivalve cell lines renders difficult virus isolation from molluscs although some viruses can be isolated in fish cell lines. Resume - Infections virales affectant les coquillages. Les mollusques bivalves sont des filteurs et de ce fait peuvent accumuler dans leurs tissus des virus d'origine anthropique. Par ailleurs, des mortalites ont ete constatees chez des bivalves en association a la detection de virus apparentes a differentes familles. Ainsi, les mortalites massives d'huitres portugaises, Crassostrea angulata, observees en France, de 1967 a 1973, ont ete associees a la presence de virus apparentes aux Iridoviridae. Des virus de type herpes ont ete detectes chez l'huitre americaine, l'huitre creuse, l'huitre plate, et plus recemment chez la coquille Saint-Jacques et la palourde japonaise. Des neoplasies touchant les cellules circulantes chez les bivalves sont encore aujourd'hui d'etiologie incertaine. Cependant, l'implication de retrovirus dans le processus a ete suspectee. Chez les coquillages, d'autres virus ont ete decrits et interpretes comme appartenant aux Papovaviridae, Togaviridae, Reoviridae, Birnaviridae et aux Picornaviridae. Cependant, l'absence de lignees cellulaires de bivalves rend difficile l'isolement de virus a partir d'echantillons de ces memes bivalves, alors que des virus peuvent etre isoles sur lignees cellullaires de poissons. Abbreviations used: GNV Gill necrosis virus, OVVD Oyster velar virus disease, HIV Hemocyte infection virus, OsHV Ostreid herpes virus, BIRP Baculovirus inhibitor of apoptosis repeat protein, MABV Marine birnavirus.
The “Goldilocks Day” for Children's Skeletal Health: Compositional Data Analysis of 24‐Hour Activity Behaviors

Optimization of children's activity behaviors for skeletal health is a key public health priority, yet it is unknown how many hours of moderate to vigorous physical activity (MVPA), light physical activity (LPA), sedentary behavior, or sleep constitute the best day—the “Goldilocks Day”—for children's bone structure and function. To describe the best day for children's skeletal health, we used data from the cross‐sectional Child Health CheckPoint. Included participants (n = 804, aged 10.7 to 12.9 years, 50% male) underwent tibial peripheral quantitative CT to assesses cross‐sectional area, trabecular and cortical density, periosteal and endosteal circumference, polar moment of inertia, and polar stress–strain index. Average daily time‐use composition (MVPA, LPA, sedentary time, and sleep) was assessed through 8‐day, 24‐hour accelerometry. Skeletal outcomes were regressed against time‐use compositions expressed as isometric log‐ratios (with quadratic terms where indicated), adjusted for sex, age, pubertal status, and socioeconomic position. The models were used to estimate optimal time‐use compositions (associated with best 5% of each skeletal outcome), which were plotted in three‐dimensional quaternary figures. The center of the overlapping area was considered the Goldilocks Day for skeletal health. Children's time‐use composition was associated with all skeletal measures (all p ≤ 0.001) except cross‐sectional area (p = 0.72). Days with more sleep and MVPA, less sedentary time, and moderate LPA were beneficially associated with skeletal measures, except cortical density, which was adversely associated. The Goldilocks daily time‐use composition for overall skeletal health was center (range): 10.9 (10.5 to 11.5) hours sleep; 8.2 (7.8 to 8.8) hours sedentary time; 3.4 (2.8 to 4.2) hours LPA, and 1.5 (1.3 to 1.5) hours MVPA. Estimated optimal sleep duration is consistent with current international guidelines (9 to 11 hours), while estimated optimal MVPA exceeds recommendations of at least 60 min/d. This first study to describe optimal durations of daily activities for children's skeletal health provides evidence to underpin guidelines. © 2020 American Society for Bone and Mineral Research (ASBMR).
Genetic manipulation of primary human natural killer cells to investigate the functional and oncogenic roles of PRDM1

Extra-nodal natural killer (NK)/T-cell lymphoma, nasal type (ENKTCL) is a highly aggressive lymphoma, in which the tumor suppressor gene PRDM1 is frequently lost or inactivated. We employed two different CRISPR/Cas9 approaches to generate PRDM1-/- primary NK cells to study the role of this gene in NK-cell homeostasis. PRDM1-/- NK cells showed a marked increase in cloning efficiency, higher proliferation rate and less apoptosis compared with their wild-type counterparts. Gene expression profiling demonstrated a marked enrichment in pathways associated with proliferation, cell cycle, MYC, MYB and TCR/NK signaling in PRDM1-/- NK cells, but pathways associated with normal cellular functions including cytotoxic functions were downregulated, suggesting that the loss of PRDM1 shifted NK cells toward proliferation and survival rather than the performance of their normal functions. We were also able to further modify a PRDM1-deleted clone to introduce heterozygous deletions of common tumor suppressor genes in ENKTCL such as TP53, DDX3X, and PTPN6. We established an in vitro model to elucidate the major pathways through which PRDM1 mediates its homeostatic control of NK cells. This approach can be applied to the study of other relevant genetic lesions and oncogenic collaborations in lymphoma pathogenesis.
Extending Absolute Pose Regression to Multiple Scenes

Direct pose regression using deep convolutional neural networks has become a highly active research area. However, even with significant improvements in performance in recent years, the best performance comes from training distinct, scene-specific networks. We propose a novel architecture, Multi-Scene PoseNet (MSPN), that allows for a single network to be used on an arbitrary number of scenes with only a small scene-specific component. Using our approach, we achieve competitive performance for two bench-mark 6DOF datasets, Microsoft 7Scenes and Cambridge Landmarks, while reducing the total number of network parameters significantly. Additionally, we demonstrate that our trained model serves as a better initialization for fine-tuning on new scenes compared to the standard ImageNet initialization, converging to lower error solutions within only a few epochs.
Detecting and Tracking Sinkholes Using Multi-Level Convolutional Neural Networks and Data Association

Sinkholes can cause severe property damage and threaten public safety. Therefore, the early prediction and detection of sinkholes are important measures for protecting both citizenry and infrastructure. Although many studies have made significant progress on sinkhole detection, challenges remain, including long-term data collection and the discovery of lightweight machine learning models that can be deployed to analyze sinkhole images. In this paper, we propose a method that takes advantage of the recent success of deep learning models to detect and track sinkholes via video streaming. Our system consists of two main stages: sinkhole detection with a cascaded convolutional neural network and sinkhole tracking with a data association algorithm. The experimental results show that a sinkhole can be tracked in real time using the dataset [1]. Furthermore, we implement the system on a Jetson TX2 embedded board (weighing 85 grams), which can operate at 13.2 FPS (frames per second). With an average IoU (intersection over union) score of 88% for sinkhole tracking and an accuracy of 97,6% for sinkhole detection on a 45-minute dataset, this study demonstrates the feasibility of sinkhole detection and tracking using IR images and their suitability for practical applications.
Segmentation and detection from organised 3D point clouds: a case study in broccoli head detection

Autonomous harvesting is becoming an important challenge and necessity in agriculture, because of the lack of labour and the growth of population needing to be fed. Perception is a key aspect of autonomous harvesting and is very challenging due to difficult lighting conditions, limited sensing technologies, occlusions, plant growth, etc. 3D vision approaches can bring several benefits addressing the aforementioned challenges such as localisation, size estimation, occlusion handling and shape analysis. In this paper, we propose a novel approach using 3D information for detecting broccoli heads based on Convolutional Neural Networks (CNNs), exploiting the organised nature of the point clouds originating from the RGBD sensors. The proposed algorithm, tested on real-world datasets, achieves better performances than the state-of-the-art, with better accuracy and generalisation in unseen scenarios, whilst significantly reducing inference time, making it better suited for real-time in-field applications.
Location- and Orientation-Aided Millimeter Wave Beam Selection Using Deep Learning

Location-aided beam alignment methods exploit the user location and prior knowledge of the propagation environment to identify the beam directions that are more likely to maximize the beamforming gain, allowing for a reduction of the beam training overhead. They have been especially popular for vehicle to everything (V2X) applications where the receive array orientation is approximately constant for each considered location, but are not directly applicable to pedestrian applications with arbitrary orientation of the user handset. This paper proposes a deep neural network based beam selection method that leverages position and orientation of the receiver to recommend a shortlist of the best beam pairs, thus significantly reducing the alignment overhead. Moreover, we use multi-labeled classification to not only capture the beam pair with highest received strength but also enrich the neural network with information of alternative beam pairs with high received signal strength, providing robustness against blockage. Simulation results show the better performance of the proposed method compared to a generalization of the inverse fingerprinting algorithm in terms of the misalignment and outage probabilities.
Deep Q-Learning based Resource Management in UAV-assisted Wireless Powered IoT Networks

In Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Internet of Things (IoT), the UAV is employed to charge the IoT nodes remotely via Wireless Power Transfer (WPT) and collect their data. A key challenge of resource management for WPT and data collection is preventing battery drainage and butter overflow of the ground IoT nodes in the presence of highly dynamic airborne channels. In this paper, we consider the resource management problem in practical scenarios, where the UAV has no a-prior information on battery levels and data queue lengths of the nodes. We formulate the resource management of UAV-assisted WPT and data collection as Markov Decision Process (MDP), where the states consist of battery levels and data queue lengths of the IoT nodes, channel qualities, and positions of the UAV. A deep Q-learning based resource management is proposed to minimize the overall data packet loss of the IoT nodes, by optimally deciding the IoT node for data collection and power transfer, and the associated modulation scheme of the IoT node.
Inflammatory Cells in Diffuse Large B Cell Lymphoma

Diffuse large B cell lymphoma (DLBCL), known as the most common non-Hodgkin lymphoma (NHL) subtype, is characterized by high clinical and biological heterogeneity. The tumor microenvironment (TME), in which the tumor cells reside, is crucial in the regulation of tumor initiation, progression, and metastasis, but it also has profound effects on therapeutic efficacy. The role of immune cells during DLBCL development is complex and involves reciprocal interactions between tumor cells, adaptive and innate immune cells, their soluble mediators and structural components present in the tumor microenvironment. Different immune cells are recruited into the tumor microenvironment and exert distinct effects on tumor progression and therapeutic outcomes. In this review, we focused on the role of macrophages, Neutrophils, T cells, natural killer cells and dendritic cells in the DLBCL microenvironment and their implication as target for DLBCL treatment. These new therapies, carried out by the induction of adaptive immunity through vaccination or passive of immunologic effectors delivery, enhance the ability of the immune system to react against the tumor antigens inducing the destruction of tumor cells.
Mapping the Transglycosylation Relevant Sites of Cold-Adapted β-d-Galactosidase from Arthrobacter sp. 32cB

β-Galactosidase from Arthrobacter sp. 32cB (ArthβDG) is a cold-adapted enzyme able to catalyze hydrolysis of β-d-galactosides and transglycosylation reaction, where galactosyl moiety is being transferred onto an acceptor larger than a water molecule. Mutants of ArthβDG: D207A and E517Q were designed to determine the significance of specific residues and to enable formation of complexes with lactulose and sucrose and to shed light onto the structural basis of the transglycosylation reaction. The catalytic assays proved loss of function mutation E517 into glutamine and a significant drop of activity for mutation of D207 into alanine. Solving crystal structures of two new mutants, and new complex structures of previously presented mutant E441Q enables description of introduced changes within active site of enzyme and determining the importance of mutated residues for active site size and character. Furthermore, usage of mutants with diminished and abolished enzymatic activity enabled solving six complex structures with galactose, lactulose or sucrose bounds. As a result, not only the galactose binding sites were mapped on the enzyme’s surface but also the mode of lactulose, product of transglycosylation reaction, and binding within the enzyme’s active site were determined and the glucopyranose binding site in the distal of active site was discovered. The latter two especially show structural details of transglycosylation, providing valuable information that may be used for engineering of ArthβDG or other analogous galactosidases belonging to GH2 family.
Genome sequencing and functional characterization of a Dictyopanus pusillus fungal enzymatic extract offers a promising alternative for lignocellulose pretreatment of oil palm residues

The pretreatment of biomass remains a critical requirement for bio-renewable fuel production from lignocellulose. Although current processes primarily involve chemical and physical approaches, the biological breakdown of lignin using enzymes and microorganisms is quickly becoming an interesting eco-friendly alternative to classical processes. As a result, bioprospection of wild fungi from naturally occurring lignin-rich sources remains a suitable method to uncover and isolate new species exhibiting ligninolytic activity. In this study, wild species of white rot fungi were collected from Colombian forests based on their natural wood decay ability and high capacity to secrete oxidoreductases with high affinity for phenolic polymers such as lignin. Based on high activity obtained from solid-state fermentation using a lignocellulose source from oil palm as matrix, we describe the isolation and whole-genome sequencing of Dictyopanus pusillus, a wild basidiomycete fungus exhibiting ABTS oxidation as an indication of laccase activity. Functional characterization of a crude enzymatic extract identified laccase activity as the main enzymatic contributor to fungal extracts, an observation supported by the identification of 13 putative genes encoding for homologous laccases in the genome. To the best of our knowledge, this represents the first report of an enzymatic extract exhibiting laccase activity in the Dictyopanus genera, offering means to exploit this species and its enzymes for the delignification process of lignocellulosic by-products from oil palm.
Estrous synchrony in a nonseasonal breeder: adaptive strategy or population process?

The idea that female mammals can manipulate the duration of each other's estrus in an effort to influence the degree of synchrony between their periods of sexual receptivity is a persistent and popularly held one. It is frequently cited as proof of pheromonal communication in humans and often invoked by models of female reproductive strategies more generally. Yet, to date, no tests of the evolutionary arguments put forward by proponents of the phenomenon have been undertaken. We addressed this deficit with an analysis of the reproductive demography of wild female chacma baboons, where variance in the temporal distribution of female receptivity is known to occur. Specifically, we tested the predictions that this variance will reflect female attempts to minimize 1) the risks of being monopolized by a single male or 2) the intensity of interfemale competition for males. Using model comparison, we found no evidence that male number or operational sex ratio had any influence on the distribution of female receptivity, the number of females in estrus, or the duration of female sexual swellings. Indeed, when modeling estrous overlap and cycling female number, we found that a simple nondeterministic model provided the best fit. We conclude, therefore, that variance in the temporal distribution of female receptivity is indicative of nothing more than a population process and that socially mediated synchrony is not a tangible adaptive phenomenon.