Miracle-dz/UniFAR-FTUs huggingface dataset:
Supplementary material to "Anthropogenic VOC in Abidjan, southern West Africa: from source quantification to atmospheric impacts"

Abstract. Several field campaigns were deployed in the framework of the project Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa (DACCIWA) to measure a broad range of atmospheric constituents. Here, we present the analysis of an unprecedented and comprehensive dataset integrating up to fifty-six VOC from ambient sites and emission sources. VOCs were collected on sorbent tubes in the coastal city of Abidjan, Côte d'Ivoire, in winter and summer 2016 and analysed by gas chromatography coupled with flame ionization and mass spectrometer detectors (GC-FID and GC-MS) at the laboratory. The comparison between VOC emission source profiles and ambient profiles suggests the substantial impact of two-wheelers and domestic fires on the composition of Abidjan's atmosphere. However, despite the high VOC concentrations near-source, moderate ambient levels were observed (by a factor of 10 to 4000 lower) and similar to the ones observed in northern mid-latitude urban areas. Beyond photochemistry, reported high wind speeds suggest that meteorology is an essential factor that regulates air pollution in Abidjan. Emission ratios (ΔVOC / ΔCO) were established based on real-world measurements achieved on a selected number of representative combustion sources. Maximal molar mass contributions were observed from two-wheeler (TW) emissions, overpassing other regional sources by two orders of magnitude. This source also largely governs the VOC atmospheric impacts in terms of OH reactivity, secondary aerosol formation (SOAP) and ozone production (POCP). While the contribution of aromatics dominates the atmospheric impact, our measurements reveal the systematic presence of anthropogenic terpenoids in all residential combustion sectors. Finally, emission factors were used to retrieve and quantify VOC emissions from the main anthropogenic source sectors at national level. Our detailed VOC emissions estimation suggests that the road transport sector is the dominant source in Cote d'Ivoire by emitting around 1200 Gg yr−1 of gas-phase VOCs. These new estimations are 100 and 160 times larger than previous global inventory estimations like MACCity or Edgar (v4.3.2). Additionally, the residential sector is also largely underestimated by a factor of 13 to 43. For the only Cote d'Ivoire, these new estimates for VOCs are three to six times higher than the whole Europe. Given the significant underestimation of VOC emissions from transport and residential sectors for Côte d'Ivoire country there is an urgent need for such an effort in the whole West Africa region for building more realistic and region-specific emission inventories. This is not only true for VOCs but all atmospheric pollutants. The dearth of waste burning, fuelwood burning and charcoal representation in regional inventories need also to be addressed, particularly in low-income areas where these types of activities are essential sources of VOC emissions.
Evolutionary history of human Plasmodium vivax revealed by genome-wide analyses of related ape parasites

Significance Chimpanzees, bonobos, and gorillas harbor close relatives of human Plasmodium vivax, but current knowledge of these parasites is limited to a small number of gene fragments derived almost exclusively from mitochondrial DNA. We compared nearly full-length genomes of ape parasites with a global sample of human P. vivax and tested the function of human and ape P. vivax proteins believed to be important for erythrocyte binding. The results showed that ape parasites are 10-fold more diverse than human P. vivax and exhibit no evidence of species specificity, whereas human P. vivax represents a bottlenecked lineage that emerged from within this parasite group. Thus, African apes represent a large P. vivax reservoir whose impact on human malaria eradication requires careful monitoring. Wild-living African apes are endemically infected with parasites that are closely related to human Plasmodium vivax, a leading cause of malaria outside Africa. This finding suggests that the origin of P. vivax was in Africa, even though the parasite is now rare in humans there. To elucidate the emergence of human P. vivax and its relationship to the ape parasites, we analyzed genome sequence data of P. vivax strains infecting six chimpanzees and one gorilla from Cameroon, Gabon, and Côte d’Ivoire. We found that ape and human parasites share nearly identical core genomes, differing by only 2% of coding sequences. However, compared with the ape parasites, human strains of P. vivax exhibit about 10-fold less diversity and have a relative excess of nonsynonymous nucleotide polymorphisms, with site-frequency spectra suggesting they are subject to greatly relaxed purifying selection. These data suggest that human P. vivax has undergone an extreme bottleneck, followed by rapid population expansion. Investigating potential host-specificity determinants, we found that ape P. vivax parasites encode intact orthologs of three reticulocyte-binding protein genes (rbp2d, rbp2e, and rbp3), which are pseudogenes in all human P. vivax strains. However, binding studies of recombinant RBP2e and RBP3 proteins to human, chimpanzee, and gorilla erythrocytes revealed no evidence of host-specific barriers to red blood cell invasion. These data suggest that, from an ancient stock of P. vivax parasites capable of infecting both humans and apes, a severely bottlenecked lineage emerged out of Africa and underwent rapid population growth as it spread globally.
PiWi-like protein 1 upregulation promotes gastric cancer invasion and metastasis

Background: PIWI-like protein 1 (PIWIL1) is an important member of the Argonaute protein family and is closely related to the malignant behaviors of tumor cells. This study aimed to investigate the relationship between PIWIL1 and gastric cancer (GC). Methods: We investigated PIWIL1 expression status in GC tissues as well as its association with clinicopathological characteristics and prognosis of GC patients. PIWIL1 siRNA was transfected into a GC cell line to elucidate its impact on malignant biological behavior. Results: The results showed that PIWIL1 was upregulated in GC tissues and correlated with tumor differentiation, lymph node status, and TNM stage. The high PIWIL1 expression was an independent predictor for the prognosis of patients with GC. Silencing of PIWIL1 expression in GC cell lines suppressed tumor cell proliferation, migration, and invasion. Conclusion: High PIWIL1 expression suggests a poor prognosis for GC patients and PIWIL1 can serve as an important molecular marker for predicting the prognosis of GC patients.
A method to estimate the number of neurons supporting visual orientation discrimination in primates [version 1; peer review: 2 approved]

In this method article, we show how to estimate of the number of retinal ganglion cells (RGC), and the number of lateral genicular nucleus (LGN) and primary visual cortex (V1) neurons involved in visual orientation discrimination tasks. We reported the results of this calculation in Kanitscheider et al . (2015), where we were interested in comparing the number of neurons in the visual periphery versus visual cortex for a specific experiment. This calculation allows estimation of the information content at different stages of the visual pathway, which can be used to assess the efficiency of the computations performed. As these numbers are generally not readily available but may be useful to other researchers, we explain here in detail how we obtained them. The calculation is straightforward, and simply requires combining anatomical and physiological information about the macaque visual pathway. Similar information could be used to repeat the calculation for other species or modalities. The Coen-Cagli et al . paper is useful, showing a reasonable way to use known physiology and anatomy to estimate the number of neurons responding to a stimulus. This may be useful towards the long term goal of figuring out how the brain puts together the activity of multiple neurons to make perceptual decisions. this believe I an appropriate level of expertise to confirm of an acceptable scientific standard. The authors provide a concise summary of the figures that go into estimating the number of neurons in retina, LGN, and V1 that are involved in a visual discrimination task. There are clearly a number of assumptions that go into such a calculation, but the authors do a good job of stating these assumptions explicitly and citing the relevant literature. One exception is the figure that 90% of RGCs project to LGN (specifically dorsal LGN in this case). This figure will also depend on eccentricity because the proportion of parasol and midget RGCs (which do project to dLGN) depends on eccentricity. In peripheral retina, the fraction of dLGN-projecting RGCs is far below 90% in primates. With this relatively simple fix, I think this short paper will provide a nice reference for calculations of cell counts in the early visual pathways.
La reprogramación en la obtención de células madre pluripotentes inducidas Reprogramming in obtaining induced pluripotent stem cells

Because of the ethical problems that have been taking for the use of embryos as a source of pluripotent cells , we have developed new sources for cells with the same characteristics. In this review we discuss some reprogramming methods that have been employed by many researchers around the world , starting with the nuclear transfer and later with the work of Yamanaka who began using the introduction of four transcription factors , Oct3 / 4 , Sox2 , Klf4 and c- Myc that originates successful reprogramming of somatic cells into induced pluripotent stem cells ( iPSC ) , which have genomic and phenotypic characteristics of embryonic stem cells , and discusses some drawbacks with these methods and the risks involve the use of the transcription factor c -Myc . Although much room for improvement in this field, iPS cells show a tremendous potential for research and potential therapeutic applications in regenerative medicine.
A Robot Leg with Compact Variable Stiffness Joint based on Leaf-Spring Mechanism

The legged robots with variable stiffness actuators (VSAs) can achieve energy-efficient and versatile locomotion. However, equipping legged robots with VSAs in real-world application is usually restricted by (i) the redundant mechanical structure design, (ii) limited stiffness variation range and speed, and (iii) high energy consumption in stiffness modulation. In this paper, we present a novel Variable-Length Leaf-Spring Actuator (VLLSA) in legged robots that aims to address the aforementioned limitations. The design is based on leaf-spring mechanism and we improve the structural design to make the proposed VSA (i) compact and lightweight in mechanical structure, (ii) precise in theoretical modeling, and (iii) capable of modulating stiffness with wide range, fast speed, and low energy consumption. Hardware experiments validate that the legged robot equipped with the proposed VLLSA has compact structure, high dynamic performance and low energy consumption.
Multi-Subdomain Adversarial Network for Cross-Subject EEG-based Emotion Recognition

The individual difference between subjects is significant in EEG-based emotion recognition, resulting in the difficulty of sharing the model across subjects. Previous studies use domain adaptation algorithms to minimize the global domain discrepancy while ignoring the class information, which may cause misalignment of subdomains and reduce model performance. This paper proposes a multi-subdomain adversarial network (MSAN) for cross-subject EEG-based emotion recognition. MSAN uses adversarial training to model the discrepancy in the global domain and subdomain to reduce the intra-class distance and enlarge the inter-class distance. In addition, MSAN initializes parameters through a pre-trained autoencoder to ensure the stability and convertibility of the model. The experimental results show that the accuracy of MSAN is improved by 30.02\% on the SEED dataset comparing with the nontransfer method.
Stochastic Configuration Machines for Industrial Artificial Intelligence

Real-time predictive modelling with desired accuracy is highly expected in industrial artificial intelligence (IAI), where neural networks play a key role. Neural networks in IAI require powerful, high-performance computing devices to operate a large number of floating point data. Based on stochastic configuration networks (SCNs), this paper proposes a new randomized learner model, termed stochastic configuration machines (SCMs), to stress effective modelling and data size saving that are useful and valuable for industrial applications. Compared to SCNs and random vector functional-link (RVFL) nets with binarized implementation, the model storage of SCMs can be significantly compressed while retaining favourable prediction performance. Besides the architecture of the SCM learner model and its learning algorithm, as an important part of this contribution, we also provide a theoretical basis on the learning capacity of SCMs by analysing the model's complexity. Experimental studies are carried out over some benchmark datasets and three industrial applications. The results demonstrate that SCM has great potential for dealing with industrial data analytics.
A Lens to Pandemic Stay at Home Attitudes

We describe the design process and the challenges we met during a rapid multi-disciplinary pandemic project related to stay-at-home orders and social media moral frames. Unlike our typical design experience, we had to handle a steeper learning curve, emerging and continually changing datasets, as well as under-specified design requirements, persistent low visual literacy, and an extremely fast turnaround for new data ingestion, prototyping, testing and deployment. We describe the lessons learned through this experience.
Holographic Cosmology $n$-point function map from the Wavefunction of the Universe

In this note we show explicitly that, applying an extension of Maldacena's map for the wavefunction of the Universe $Z[\phi]=\Psi[\phi]$ from de Sitter inflation to holographic cosmology, we find the relations previously derived for 2-point functions, $\langle h_{ij}(p) h_{kl}(-p)\rangle \sim 1/[{\rm Im}\langle T_{ij}(-ip)T_{kl}(+ip)\rangle]$ and a similar one for currents, which were used to show that holographic cosmology matches CMBR data and solves Big Bang problems as well as inflation. Higher point functions are done similarly, and as an application, we check the result for the 3-point functions of scalar and tensor fluctuations and find the result for the monopole non-Gaussianity arising from the 3-point functions of currents. The method is simple and potentially could be applied to calculate any observable in holographic cosmology.
Adversarial Predictions of Data Distributions Across Federated Internet-of-Things Devices

Federated learning (FL) is increasingly becoming the default approach for training machine learning models across decentralized Internet-of-Things (IoT) devices. A key advantage of FL is that no raw data are communicated across the network, providing an immediate layer of privacy. Despite this, recent works have demonstrated that data reconstruction can be done with the locally trained model updates which are communicated across the network. However, many of these works have limitations with regard to how the gradients are computed in backpropagation. In this work, we demonstrate that the model weights shared in FL can expose revealing information about the local data distributions of IoT devices. This leakage could expose sensitive information to malicious actors in a distributed system. We further discuss results which show that injecting noise into model weights is ineffective at preventing data leakage without seriously harming the global model accuracy.
An Ensemble Approach to Personalized Real Time Predictive Writing for Experts

Completing a sentence, phrase or word after typing few words / characters is very helpful for Intuit financial experts, while taking notes or having a live chat with users, since they need to write complex financial concepts more efficiently and accurately many times in a day. In this paper, we tie together different approaches like large language models, traditional Markov Models and char level models to create an end-to-end system to provide personalised sentence/word auto-complete suggestions to experts, under strict latency constraints. Proposed system can auto-complete sentences, phrases or words while writing with personalisation and can be trained with very less data and resources with good efficiency. Our proposed system is not only efficient and personalized but also robust as it leverages multiple machine learning techniques along with transfer learning approach to fine tune large language model with Intuit specific data. This ensures that even in cases of rare or unusual phrases, the system can provide relevant auto-complete suggestions in near real time. Survey has showed that this system saves expert note-taking time and boosts expert confidence in their communication with teammates and clients. Since enabling this predictive writing feature for QBLive experts, more than a million keystrokes have been saved based on these suggestions. We have done comparative study for our ensemble choice. Moreover this feature can be integrated with any product which has writing facility within a very short period of time.
The Logarithmic Quot space: foundations and tropicalisation

We construct a logarithmic version of the Hilbert scheme, and more generally the Quot scheme, of a simple normal crossings pair. The logarithmic Quot space admits a natural tropicalisation called the space of tropical supports, which is a functor on the category of cone complexes. The fibers of the map to the space of tropical supports are algebraic. The space of tropical supports is representable by ``piecewise linear spaces'', which are introduced here to generalise fans and cone complexes to allow non--convex geometries. The space of tropical supports can be seen as a polyhedral analogue of the Hilbert scheme. The logarithmic Quot space parameterises quotient sheaves on logarithmic modifications that satisfy a natural transversality condition. We prove that the space it is a logarithmic algebraic space, is separated, and universally closed. The logarithmic Hilbert space parameterizes families of proper monomorphisms, and in this way is exactly analogous to the classical Hilbert scheme. The new complexity of the space can then be viewed as stemming from the complexity of proper monomorphisms in logarithmic geometry. Our construction generalises the logarithmic Donaldson--Thomas space studied by Maulik--Ranganathan to arbitrary rank and dimension, and the good degenerations of Quot schemes of Li--Wu to simple normal crossings geometries.
RefSearch: A Search Engine for Refactoring

Developers often refactor source code to improve its quality during software development. A challenge in refactoring is to determine if it can be applied or not. To help with this decision-making process, we aim to search for past refactoring cases that are similar to the current refactoring scenario. We have designed and implemented a system called RefSearch that enables users to search for refactoring cases through a user-friendly query language. The system collects refactoring instances using two refactoring detectors and provides a web interface for querying and browsing the cases. We used four refactoring scenarios as test cases to evaluate the expressiveness of the query language and the search performance of the system. RefSearch is available at https://github.com/salab/refsearch.
Drug Interaction Vectors Neural Network: DrIVeNN

Polypharmacy, the concurrent use of multiple drugs to treat a single condition, is common in patients managing multiple or complex conditions. However, as more drugs are added to the treatment plan, the risk of adverse drug events (ADEs) rises rapidly. Many serious ADEs associated with polypharmacy only become known after the drugs are in use. It is impractical to test every possible drug combination during clinical trials. This issue is particularly prevalent among older adults with cardiovascular disease (CVD) where polypharmacy and ADEs are commonly observed. In this research, our primary objective was to identify key drug features to build and evaluate a model for modeling polypharmacy ADEs. Our secondary objective was to assess our model on a domain-specific case study. We developed a two-layer neural network that incorporated drug features such as molecular structure, drug-protein interactions, and mono drug side effects (DrIVeNN). We assessed DrIVeNN using publicly available side effect databases and determined Principal Component Analysis (PCA) with a variance threshold of 0.95 as the most effective feature selection method. DrIVeNN performed moderately better than state-of-the-art models like RESCAL, DEDICOM, DeepWalk, Decagon, DeepDDI, KGDDI, and KGNN in terms of AUROC for the drug-drug interaction prediction task. We also conducted a domain-specific case study centered on the treatment of cardiovascular disease (CVD). When the best performing model architecture was applied to the CVD treatment cohort, there was a significant increase in performance from the general model. We observed an average AUROC for CVD drug pair prediction increasing from 0.826 (general model) to 0.975 (CVD specific model). Our findings indicate the strong potential of domain-specific models for improving the accuracy of drug-drug interaction predictions.
Challenges of GPT-3-Based Conversational Agents for Healthcare

The potential of medical domain dialogue agents lies in their ability to provide patients with faster information access while enabling medical specialists to concentrate on critical tasks. However, the integration of large-language models (LLMs) into these agents presents certain limitations that may result in serious consequences. This paper investigates the challenges and risks of using GPT-3-based models for medical question-answering (MedQA). We perform several evaluations contextualized in terms of standard medical principles. We provide a procedure for manually designing patient queries to stress-test high-risk limitations of LLMs in MedQA systems. Our analysis reveals that LLMs fail to respond adequately to these queries, generating erroneous medical information, unsafe recommendations, and content that may be considered offensive.
Constructing Population-Specific Atlases from Whole Body MRI: Application to the UKBB

Population atlases are commonly utilised in medical imaging to facilitate the investigation of variability across populations. Such atlases enable the mapping of medical images into a common coordinate system, promoting comparability and enabling the study of inter-subject differences. Constructing such atlases becomes particularly challenging when working with highly heterogeneous datasets, such as whole-body images, where subjects show significant anatomical variations. In this work, we propose a pipeline for generating a standardised whole-body atlas for a highly heterogeneous population by partitioning the population into meaningful subgroups. We create six whole-body atlases that represent a healthy population average using magnetic resonance (MR) images from the UK Biobank dataset. We furthermore unbias them, and this way obtain a realistic representation of the population. In addition to the anatomical atlases, we generate probabilistic atlases that capture the distributions of abdominal fat and five abdominal organs across the population. We demonstrate different applications of these atlases, using the differences between subjects with medical conditions such as diabetes and cardiovascular diseases and healthy subjects from the atlas space. With this work, we make the constructed anatomical and label atlases publically available and anticipate them to support medical research conducted on whole-body MR images.