Miracle-dz/UniFAR-FTUs huggingface dataset:
Survival Associations between Patient Age and Treatment Modality in Olfactory Neuroblastoma: A Retrospective Population-Based Study

Olfactory neuroblastoma (ONB) is a rare neuroepithelial-derived malignancy that usually presents in the nasal cavity. The rarity of ONB has led to conflicting reports regarding associations of patient age and ONB survival and outcome. Moreover, long-term outcomes of chemotherapy and other treatment modalities are speculated. Here, we aimed to compare survival outcomes across age groups through time and determine associations between treatment modality and survival. In this retrospective population-based study, we analyzed the SEER 2000–2016 Database for patients with ONB tumors. Using Kaplan–Meier survival analysis, a significant effect of age and cancer-specific survival (CSS) was observed; geriatric ONB patients had the lowest CSS overall. Generalized linear models and survival analyses demonstrated that CSS of the pediatric patient population was similar to the geriatric group through 100 months but plateaued thereafter and was the highest of all age groups. Radiation and surgery were associated with increased CSS, while chemotherapy was associated with decreased CSS. GLM results showed that tumor grade, stage and lymph node involvement had no CSS associations with age or treatment modality. Our results provide insight for future investigations of long-term outcomes associated with ONB patient age and treatment modality, and we conclude that survival statistics of ONB patients should be analyzed in terms of trends through time rather than fixed in time.
Hydroscapes: A Useful Metric for Distinguishing Iso-/Anisohydric Behavior in Almond Cultivars

As a consequence of climate change, water scarcity has increased the use of the iso-/anisohydric concept with the aim of identifying anisohydric or drought-tolerant genotypes. Recently, Meinzer and colleagues developed a metric for discriminating between iso- and anisohydric behavior called the hydroscape, which describes a range in which stomata control leaf water potential (Ψ) with decreasing water availability, and it is linked to several water-regulation and drought-tolerance traits. Thus, our objective was to test the usefulness of the hydroscape in discriminating between iso- and anisohydric Prunus dulcis cultivars, a species that is widely cultivated in Mediterranean central Chile due to its ability to withstand water stress. Through a pot desiccation experiment, we determined that the hydroscape was able to discriminate between two contrasting Prunus cultivars; the more anisohydric cultivar had a hydroscape 4.5 times greater than that of the other cultivar, and the hydroscape correlated with other metrics of plant water-use strategies, such as the maximum range of daily Ψ variation and the Ψ at stomatal closure. Moreover, the photosynthesis rates were also differently affected between cultivars. The more isohydric cultivar, which had a smaller hydroscape, displayed a steeper photosynthesis reduction at progressively lower midday Ψ. This methodology could be further used to identify drought-tolerant anisohydric Prunus cultivars.
Potential Biomarkers in Diagnosis of Renal Acanthamoebiasis

Recent studies indicate that Acanthamoeba spp. may play a significant role in kidney dysfunction. The aim of the study was to examine the levels of kidney injury molecule 1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), and monocyte chemotactic protein 1 (MCP-1), as well as an activity of matrix metalloproteinases 2 and 9 (MMP-2 and MMP-9, respectively) in the kidneys of immunocompetent and immunosuppressed mice infected with Acanthamoeba spp. The levels of KIM-1, NGAL, and MCP-1 were analyzed by enzyme-linked immunosorbent assay (ELISA), and the activity of MMPs was determined by gelatin zymography. The elevated KIM-1 level was found in the kidneys of immunocompetent mice at the beginning of Acanthamoeba spp. infection. In the immunosuppressed mice, the KIM-1 level was statistically different. The statistically decreased NGAL level was found in the kidneys of immunocompetent mice compared to the uninfected mice. In the immunocompromised mice, we found statistically significant differences in MCP-1 levels between the uninfected and infected groups. There was an increase in the expression of both MMP-2 and MMP-9 in the kidneys of immunocompetent and immunosuppressed mice infected with Acanthamoeba spp. compared to the uninfected mice. The results indicate that KIM-1, NGAL, MCP-1, MMP-2, MMP-9, and MMP-9/NGAL might be promising biomarkers of renal acanthamoebiasis.
Fluid Intake Monitoring Systems for the Elderly: A Review of the Literature

Fluid intake monitoring is an essential component in preventing dehydration and overhydration, especially for the senior population. Numerous critical health problems are associated with poor or excessive drinking such as swelling of the brain and heart failure. Real-time systems for monitoring fluid intake will not only measure the exact amount consumed by the users, but could also motivate people to maintain a healthy lifestyle by providing feedback to encourage them to hydrate regularly throughout the day. This paper reviews the most recent solutions to automatic fluid intake monitoring both commercially and in the literature. The available technologies are divided into four categories: wearables, surfaces with embedded sensors, vision- and environmental-based solutions, and smart containers. A detailed performance evaluation was carried out considering detection accuracy, usability and availability. It was observed that the most promising results came from studies that used data fusion from multiple technologies, compared to using an individual technology. The areas that need further research and the challenges for each category are discussed in detail.
A Simple Neural Network for Collision Detection of Collaborative Robots

Due to the epidemic threat, more and more companies decide to automate their production lines. Given the lack of adequate security or space, in most cases, such companies cannot use classic production robots. The solution to this problem is the use of collaborative robots (cobots). However, the required equipment (force sensors) or alternative methods of detecting a threat to humans are usually quite expensive. The article presents the practical aspect of collision detection with the use of a simple neural architecture. A virtual force and torque sensor, implemented as a neural network, may be useful in a team of collaborative robots. Four different approaches are compared in this article: auto-regressive (AR), recurrent neural network (RNN), convolutional long short-term memory (CNN-LSTM) and mixed convolutional LSTM network (MC-LSTM). These architectures are analyzed at different levels of input regression (motor current, position, speed, control velocity). This sensor was tested on the original CURA6 robot prototype (Cooperative Universal Robotic Assistant 6) by Intema. The test results indicate that the MC-LSTM architecture is the most effective with the regression level set at 12 samples (at 24 Hz). The mean absolute prediction error obtained by the MC-LSTM architecture was approximately 22 Nm. The conducted external test (72 different signals with collisions) shows that the presented architecture can be used as a collision detector. The MC-LSTM collision detection f1 score with the optimal threshold was 0.85. A well-developed virtual sensor based on such a network can be used to detect various types of collisions of cobot or other mobile or stationary systems operating on the basis of human-machine interaction.
CUMULATOR — a tool to quantify and report the carbon footprint of machine learning computations and communication in academia and healthcare

"The cost of training machines is becoming a problem". This is the title of an article from The Economist published in June 2020 that highlights the staggeringly unappreciated the financial impact of AI [38]. However, these costs on are not limited to monetary concerns but also accrue a concerning environmental toll. One round of training for some of the most complex machine learning models can emit millions of kilograms of carbon dioxide due to the electricity consumed . With the growing popularity of ML and the digitization across all sectors, there should be a growing awareness of the potential impact of these technologies on the environment and their potential contribution to climate change. Then, their use could be rationalized and steps can be taken to responsibilize the offset of their impact. We hereby propose CUMULATOR: an open-source API to quantify and report the carbon footprint of machine learning methods. As a demonstration, we integrated this API within an ML based medical research platform called Alg-E, which will be used in a large scale medical research project in Tanzania and Rwanda. We use CUMULATOR to analyse the trade-off between accuracy and carbon footprint within Alg-E and extend it with simple visualisations. In parallel, we also propose a Carbon Statement Protocol to quantify and report the carbon footprint of individual work, which uses this project as a proof-of-concept. This protocol and CUMULATOR thus comprise a great set of tools to report the carbon footprint of a large-scale medical research trial and EPFL research projects in the future.
Interpretable Event Detection and Extraction using Multi-Aspect Attention

Classical encoding and extraction methods rely on fixed dictionary of keywords and templates or require ground truth labels for phrase/sentences. This prevents wide spread use of these information encoding approaches to large-scale free form (unstructured) text available on the web. Event encoding can be categorized as a hierarchical task where the coarser level task is event detection – identification of documents containing a specific event and the fine-grained task of event encoding – identifying key phrases, key sentences. Hierarchical models with attention seem like a natural choice for this problem, given their ability to differentially attend to more or less important features when constructing the document representation. In this work we present a novel factorized bilinear multi-aspect attention mechanism (FBMA) that attends to different aspects of text while constructing its representation. We use this mechanism within a hierarchical framework to extract event references or extents i.e. event-related sentences and trigger words from the text. We find that our approach outperforms state-of-the-art baselines for event detection on the Civil Unrest and Military Action and Non-State Actor datasets in two different languages. We further provide qualitative examples of the extracted event extents and trigger words giving a peek into what our model learns.
High Impact Learning for Facilitator Training and Development

Research shows that cascade training models are common approaches to improving teaching in many developing countries. Cascade models are popular for reaching large cohorts of teachers in a short time and often at a low cost. However, they have been criticized because training efforts can get diluted and transmission modes of instruction tend to prevail across all tiers of the cascade. In this essay, we discuss the development and implementation of the Facilitator Training Program (FTP) at a university in Pakistan. The FTP supports high impact learning for new facilitators to develop pedagogical knowledge and instructional and facilitation skills needed to sustain an experiential faculty development program. We define high impact learning as experiential, reflective, emphasizing feedback, and involving learning with and from others. We believe the FTP design can be useful for facilitator training in other institutions outside of Pakistan where cascade training models may also be prevalent.
Generalized Comprehensive Motion Theory for High-Order Differential Dynamics

—We address the problem of calculating complex Jacobian matrices that can arise from optimization problems. An example is the inverse optimal control in human motion analysis which has a cost function that depends on the second order time-derivative of torque ¨ τ . Thus, its gradient decomposed to, among other, the Jacobian δ ¨ τ/δq . We propose a new concept called N -order Comprehensive Motion Transformation Matrix ( N -CMTM) to provide an exact analytical solution of several Jacobians. The computational complexity of the basic Jacobian and its N -order time-derivatives computed from the N -CMTM is experimentally shown to be linear to the number of joints N j . The N -CMTM is based on well-known spatial algebra which makes it available for any type of robots. Moreover, it can be used along classical algorithms. The computational complexity of the construction of the N -CMTM itself is experimentally shown to be N 2 .
PROMPT: Probabilistic Motion Primitives based Trajectory Planning

We present a novel approach to motion planning for autonomous ground vehicles by formulating motion primitives as probabilistic distributions of trajectories (aka probabilistic motion primitives - ProMP) and performing stochastic optimisation on them for finding an optimal path. We show that compared to the traditional approach of using discrete motion primitives or direct stochastic optimisation of the whole path, incorporating ProMPs enables higher quality of paths by enabling constraint conditioning, combination and blending of probability distributions. We present two motion planners utilizing this approach: feasibility-based trajectory sampling (PROMPT-S) and stochastic gradient-based trajectory optimisation (PROMPT-O). We show simulation results of our approach outperforming state-of-the-art optimisation as well as discrete motion primitives-based planners. We additionally illustrate the versatility of our approach by showing PROMPT’s ability to handle significantly skewed motion primitives, e.g, as induced by steering failure in AGVs as well as the composition of motion primitives to perform complex maneuvers. Finally, we demonstrate the practicality of these planners by implementing them on a real self-driving vehicle navigating on structured and unstructured off-road terrains.
Anterolateral thigh osteomyocutaneous flap in head and neck: Lessons learned

Concerns regarding iatrogenic femur fracture may deter adoption of the anterolateral thigh osteomyocutaneous (ALTO) flap as an alternative reconstructive technique for large composite defects of the head and neck. We describe the evolution of our experience with this flap and the lessons learned in femur management.
The effects of livestock methane emission on the global warming: a review

The greenhouse gas (GHG) emissions from the agricultural sector account for about 25.5% of total global anthropogenic emission. Methane is one of the most important GHGs and it has 21 times more global warming potential than carbon dioxide. Ruminant livestock contributes the major proportion of total agricultural emission of methane. The CH4 produced by ruminants is not only related to environmental problems, but is also associated with energy losses. This article reviews various attempts to reduce methane emission, mainly through improved genetic selection, modification of dietary composition, or through rumen microbial manipulation. Vaccines against methanogenic bacteria or monensin as antimicrobial are widely used in ruminants to improve performance. Increases in the understanding of the environmental impacts of livestock farming and methods of mitigating them are likely to occur and more effective approaches to designing systems of mitigation will be developed.
Note on a stepwise procedure for rejecting at least k out of m hypotheses: A simple Holm‐type formulation and proof

Mielke et al. (2021) proposed a stepwise multiple testing procedure (MTP) based on marginal p‐values for rejecting at least k out of m null hypotheses. Briefly, the MTP cannot reject less than k hypotheses, but this property improves the power to reject k or more hypotheses relative to the stepdown MTP of Holm (1979). Mielke et al. discussed why such an MTP is of interest in the context of biosimilarity developments. This article describes how a slight modification of Holm's simple direct arguments can be used as an alternative to the closed‐testing arguments of Mielke et al. to show the strong control of the family‐wise error rate. This modification is based on a Holm‐type formulation of the rejection algorithm where each step involves a single ordered p‐value. With k equal to one, the stepwise MTP reduces to Holm's stepdown MTP. The MTP is valid quite generally. A version of the MTP with weights for hypotheses is also described and discussed. As in the case without weights: (a) a modification of Holm's arguments can be used; (b) with k equal to one, the MTP reduces to Holm's MTP with weights; and (c) the MTP is valid quite generally.
Adsorption of β-glucuronidase and estrogens on pectin/lignin hydrogel particles

ABSTRACT The objective of this study was to generate supramolecular complexes of pectin and lignin with properties necessary to interrupt the enterohepatic circulation (EHC) of estrogens. Three types of pectin hydrogel particles were prepared from citrus pectin (PHP), citrus pectin/wheat lignin (PLWHP), or citrus pectin/oak lignin (PLOHP). The surface of PHP was smooth, whereas the surface of PLWHP had irregularly arranged structures and PLOHP contained pores. PHP actively adsorbed microbial β-glucuronidase and moderately adsorbed estrogens. PLWHP had moderate and PLOHP high affinity for estrogens. Thus, PLOHP meets the requirements of a complex capable of interrupting the EHC of estrogens. GRAPHICAL ABSTRACT
Culture‐enriched community profiling improves resolution of the vertebrate gut microbiota

Vertebrates harbour gut microbial communities containing hundreds of bacterial species, most of which have never been cultivated or isolated in the laboratory. The lack of cultured representatives from vertebrate gut microbiotas limits the description and experimental interrogation of these communities. Here, we show that representatives from >50% of the bacterial genera detected by culture‐independent sequencing in the gut microbiotas of fence lizards, house mice, chimpanzees, and humans were recovered in mixed cultures from frozen faecal samples plated on a panel of nine media under a single growth condition. In addition, culturing captured >100 rare bacterial genera overlooked by culture‐independent sequencing, more than doubling the total number of bacterial sequence variants detected. Our approach recovered representatives from 23 previously uncultured candidate bacterial genera, 12 of which were not detected by culture‐independent sequencing. Results identified strategies for both indiscriminate and selective culturing of the gut microbiota that were reproducible across vertebrate species. Isolation followed by whole‐genome sequencing of 161 bacterial colonies from wild chimpanzees enabled the discovery of candidate novel species closely related to the opportunistic pathogens of humans Clostridium difficile and Hungatella hathewayi. This study establishes culturing methods that improve inventories and facilitate isolation of gut microbiota constituents from a wide diversity of vertebrate species.
ADIÇÃO DE FONTE DE CARBONO NO INÍCIO DA FASE ANÓXICA NA REMOÇÃO DE MACRONUTRIENTES E DQO POR LODO ATIVADO USANDO RBS NO TRATAMENTO DE ÁGUA RESIDUÁRIA AVÍCOLA

Several strategies of sequencing batch reactor operation were utilised to promote carbonaceous material and nutrients removal from poultry wastewater by activated sludge. The reactor started with 500 mL of sludge suspension or 500 mL of sludge suspension plus 1500 mL of raw wastewater, being fed up to 4 litres. The operation strategy that led to the best results was: initial volume of sludge suspension ‐ 500 mL, aerated feeding ‐ 4 h, aerated reaction ‐ 7.25 h and sludge settling ‐ 1 h. The dissolved oxygen concentration stayed between 2 and 5 mg/L and the COD, ammonia, nitrite, nitrate and phosphorus removal reached were 81, 83, 54, 72 and 86%, respectively, at 25 o C.
Stacked Autoencoder based Feature Compression for Optimal Classification of Parkinson Disease from Vocal Feature Vectors using Immune Algorithms

Parkinson’s disease (PD) is a neurological progressive disorder and is most common among people who are above 60 years old. It affects the brain nerve cells due to the deficiency of dopamine secretion. Dopamine acts as a neurotransmitter and helps in the movement of the body parts. Once brain cells/neurons start dying due to aging, then it will lead to a decrease in dopamine levels. The symptoms of Parkinson’s are difficultly in doing regular/habitual movements, uncontrollable shaking of hands and limbs may encounter memory loss, stiff muscles, sudden temporary loss of control, etc. The severity of the disease will be worse if not diagnosed and treated at the early stages. This paper concentrates on developing Parkinson’s disease diagnosing system using machine learning techniques and algorithms. Machine Learning is an integral part of artificial intelligence it takes huge data as input and train by making use of existing algorithms to understand the pattern of the data. Based on the recognized pattern, the machine will act accordingly without any human intervention. In this work, two major approaches have been employed to diagnose PD. Initially, 26 vocal data of PD affected and healthy individual datasets are obtained from the UCI Machine Learning data repository, are taken as initial raw data/features. In pre-processing, the mRMR feature selection algorithm is employed to minimize the feature count and increase the accuracy rate. The selected features will further be extracted using the Stacked Autoencoder technique to improve and increase the accuracy rate and quality of classification with reduced run time. K-fold cross-validation is used to evaluate the predictive capability of the model and the effectiveness of the extracted features. Artificial Immune Recognition System – Parallel (AIRS-P), an immune inspired algorithm is employed to classify the data from the extracted features. The proposed system attained 97% accuracy, outperforms the benchmarked algorithms and proved its significance on PD classification. Keywords—Immune algorithms; Parkinson’s disease; stacked autoencoder; airs-parallel; machine learning
Synthesis and characterization of blue-violet emitting iridium(III) complex coordinated via chlorinated ancillary ligand

Abstract Refluxing HL [5-chloro-2-((2-hydroxynaphthalen-1-yl)diazenyl)phenyl)(phenyl)methanone] with [(2-ppy)2Ir(μ-Cl)]2 in ethanol-dichloromethane mixture in the presence of a base (NEt3) afforded complex of composition [Ir(2-ppy)2(L)], where the azo-ligand is coordinated to iridium(III) center as monoanionic bidentate N,O-donors. The ligand and complex were characterized unequivocally. Molecular structures of both the ligand and complex were confirmed by single-crystal X-ray diffraction. The ground and excited-state geometries, absorption, and emission properties of the iridium(III) complex were further examined by DFT and TD-DFT methods. The complex exhibits a blue-violet emission band at 395 nm with quantum yield (Φ) 0.06. Experimental and theoretical studies support the fact that the nature of emission is an admixture of 3IL and 3MLCT state. Graphical Abstract