Miracle-dz/UniFAR-FTUs huggingface dataset:
Frequency of diagnostic combination of risk factors of metabolic syndrome associated with diabetes, hypertension, chronic kidney disease and apparent health

Metabolic syndrome (MetS) has been variously defined because of varying emphasis placed on its components - dyslipidaemia, hypertension (HBP), hyperglyecmia (HG) and abnormal weight. MetS may be associated with diabetes (DM), HBP, chronic kidney disease (CKD) and apparent health (AH). Unfortunately, the frequency of the diagnostic combination of the risk factors in these conditions has not been reported because it could have therapeutic implications. Data generated in earlier studies on 1107 subjects were analyzed. MetS was diagnosed according to the NCEP-ATP 111 criteria. The frequency of the ten possible diagnostic combinations of the risk factor of MetS was counted and analyzed in each condition associated with the syndrome. The triad, truncal obesity (TO), HBP and decreased high density lipoprotein cholesterol (LHDC) recorded the highest frequency in AH subjects, (males 58.8 and females 38.1%) Among the male patients, HBP, HG and decreased LHDC had the highest frequency (DM, 29.9%; HBP 31.8%; CKD 35.5%). The frequency of the diagnostic triads in the female patients differed from that of the males. The combination of TO, HBP and hypertriglyceridaemia (HTG) absent among the male patients gave the highest frequency in female hypertension and CKD patients. The risk factors combination of TO, HBP and LHDLC can be used for screening MetS in the study population especially among the AH subjects. Moreover, TO appears to be the single most significant risk factor for MetS in the population.
“I Don’t Do Anything; I’m Just Being Taken Care Of”: Experiences of Patients and Their Caregivers Transitioning Back into the Community Following Traumatic Injury in Northern Tanzania

After discharge from the hospital for traumatic injury, patients and their caregivers face a period of increased vulnerability. This adjustment phase is poorly characterized, especially in low- and middle-income countries. We explored the experiences of patients and their caregivers in Northern Tanzania after hospitalization for a traumatic injury. Patients who received care for traumatic injury at the Kilimanjaro Christian Medical Center and their caregivers were selected as part of a convenience sample from January 2019 to December 2019. Analysts developed a codebook; content and analytic memos were subsequently created. We then applied the biopsychosocial model to further characterize our findings. Participants included 26 patients and 11 caregivers. Patients were mostly middle-aged (mean age 37.7) males (80.8%), residing in urban settings (57.7%), injured in road traffic accidents (65.4%), and who required surgery (69.2%). Most caregivers were female. Seven major themes arose: pain, decreased physical functioning, poor emotional health, lack of support, challenges with daily activities, financial strain, and obstacles to accessing healthcare. This study describes some of the difficulties transitioning back into the community after hospitalization for traumatic injury. Our work demonstrates the importance of mixed methods approaches in characterizing and addressing transitions of care challenges.
Effect of Hypoxia-Induced Micro-RNAs Expression on Oncogenesis

MicroRNAs (miRNAs) are small non-coding RNAs that negatively regulate gene expression at the post-transcriptional level. An aberrant regulation of gene expression by miRNAs is associated with numerous diseases, including cancer. MiRNAs expression can be influenced by various stimuli, among which hypoxia; however, the effects of different types of continuous hypoxia (moderate or marked) on miRNAs are still poorly studied. Lately, some hypoxia-inducible miRNAs (HRMs, hypoxia-regulated miRNAs) have been identified. These HRMs are often activated in different types of cancers, suggesting their role in tumorigenesis. The aim of this study was to evaluate changes in miRNAs expression both in moderate continuous hypoxia and marked continuous hypoxia to better understand the possible relationship between hypoxia, miRNAs, and colorectal cancer. We used RT-PCR to detect the miRNAs expression in colorectal cancer cell lines in conditions of moderate and marked continuous hypoxia. The expression of miRNAs was analyzed using a two-way ANOVA test to compare the differential expression of miRNAs among groups. The levels of almost all analyzed miRNAs (miR-21, miR-23b, miR-26a, miR-27b, and miR-145) were greater in moderate hypoxia versus marked hypoxia, except for miR-23b and miR-21. This study identified a series of miRNAs involved in the response to different types of continuous hypoxia (moderate and marked), highlighting that they play a role in the development of cancer. To date, there are no other studies that demonstrate how these two types of continuous hypoxia could be able to activate different molecular pathways that lead to a different expression of specific miRNAs involved in tumorigenesis.
THE SHEFFIELD UNIVERSITY SYSTEM FOR THE MIREX 2020:LYRICS TRANSCRIPTION TASK

This extended abstract describes the system we submitted to the MIREX 2020 Lyrics Transcription task. The system consists of two modules: a source separation front-end and an ASR back-end. The ﬁrst module separates the vocal from a polyphonic song by utilising a convolutional time-domain audio separation network (ConvTasNet). The second module transcribes the lyrics from the separated vocal by using a factored-layer time-delay neural network (fTDNN) acoustic model and a 4-gram language model. Both the separation and the ASR modules are trained on a large open-source singing corpora, namely, Smule DAMP-VSEP and Smule DAMP-MVP. Using a separation module audio pre-processing reduced the transcription error by roughly 11% absolute WER for polyphonic songs compared with transcriptions without vocal separation. However, the best WER achieved was 52.06%, very high compared to WERs as low as 19.60% that we achieved previously for unaccompanied song [16].
Impacts of Heterogeneous Chemistry on Vertical Profiles of Martian Ozone

We show a positive vertical correlation between ozone and water ice using a vertical cross‐correlation analysis with observations from the ExoMars Trace Gas Orbiter's Nadir and Occultation for Mars Discovery instrument. This is particularly apparent during LS = 0°–180°, Mars Year 35 at high southern latitudes, when the water vapor abundance is low. Ozone and water vapor are anti‐correlated on Mars; Clancy et al. (2016, https://doi.org/10.1016/j.icarus.2015.11.016) also discuss the anti‐correlation between ozone and water ice. However, our simulations with gas‐phase‐only chemistry using a 1‐D model show that ozone concentration is not influenced by water ice. Heterogeneous chemistry has been proposed as a mechanism to explain the underprediction of ozone in global climate models (GCMs) through the removal of HOx. We find improving the heterogeneous chemical scheme by creating a separate tracer for the HOx adsorbed state, causes ozone abundance to increase when water ice is present (30–50 km), better matching observed trends. When water vapor abundance is high, there is no consistent vertical correlation between observed ozone and water ice and, in simulated scenarios, the heterogeneous chemistry has a minor influence on ozone. HOx, which are by‐products of water vapor, dominate ozone abundance, masking the effects of heterogeneous chemistry on ozone, and making adsorption of HOx have a negligible impact on ozone. This is consistent with gas‐phase‐only modeled ozone, showing good agreement with observations when water vapor is abundant. Overall, the inclusion of heterogeneous chemistry improves the ozone vertical structure in regions of low water vapor abundance, which may partially explain GCM ozone deficits.
Can Language Models Take A Hint? Prompting for Controllable Contextualized Commonsense Inference

Generating commonsense assertions, given a 001 certain story context, is a tough challenge 002 even for modern language models. One of 003 the reasons for this may be that the model 004 has to "guess" what topic or entity in a story 005 to generate an assertion about. Prior work 006 has tackled part of the problem, by provid-007 ing techniques to align commonsense infer-008 ences with stories and training language gen-009 eration models on these. However, none of 010 the prior work provides means to control the 011 parts of a generated assertion. In this work, 012 we present "hinting", a data augmentation tech-013 nique for improving inference of contextual-014 ized commonsense assertions. Hinting is a pre-015 ﬁx prompting strategy that uses both hard and 016 soft prompts. We demonstrate the effective-017 ness of hinting by showcasing its effect on two 018 contextual commonsense inference datasets: 019 ParaCOMET (Gabriel et al., 2021) and GLU-020 COSE (Mostafazadeh et al., 2020), for both 021 general and context-speciﬁc inference. 022
A Derivation of Optimization Objectives

For a policy π(a|s) and a dynamics model T (s′|s, a), its occupancy measure ρπ : S ×A → R can be defined as ρπ (s, a) = π(s, a) ∑∞ i=0 γ i Pr{si = s|π, T}, where S and A denote the state and action space respectively, and Pr{si = s|π, T} is the probability of si = s under policy π and dynamics model T (s′|s, a) at time-step i [1]. The occupancy measure can be interpreted as the distribution of state-action pairs that an agent encounters when navigating the dynamics model T with policy π [2]. The primitive objective of unsupervised domain adaptation (UDA) is to minimize the data distribution in the source domain and the mapped data distribution from the target domain. From the perspective of policy occupancy measure, it is to learn p(s|o) to minimize
The prediction of forest carbon sequestration dynamics in Guizhou Province and relevant influencing factors.

Guizhou Province in China is a typical karst region. Because of its fragile environment, the restoration of the vegetation and environment of this province is highly important. In this study, biomass expansion factors and average biomass were used to measure the forest biomass, which we used to fit a logistic regression between carbon density and forest age for each forest type based on a 2010 forest inventory in Guizhou. In combination with the Guizhou afforestation plan, the model predicts the trends in carbon storage, carbon density and carbon sequestration rate. In addition, we used grey relational analysis and multiple regression to explore the effects of afforestation, forest age and site conditions. The model demonstrated, first, that forest carbon storage in Guizhou is predicted to gradually increase from 203.62 TgC in 2010 to 575.99 TgC in 2050; second, forest carbon density is also predicted to gradually increase from 29.32 MgC•hm in 2010 to 55.64 MgC•hm in 2050; and finally, afforestation is predicted to significantly improve the forest carbon sequestration rate from 0.72 MgC•hm•a in 2010 to 0.77 MgC•hm•a in 2050. The expansion of forest area and the increase in forest age resulted from afforestation and promoted forest carbon sinks. Forest carbon sequestration increased significantly with increasing forest age but was strongly constrained by rocky desertification. In addition to afforestation, we suggest that improving the forest carbon sink in Guizhou should put a greater emphasis on approaches such as fully exploiting the carbon sequestration capacity of the existing forests, decreasing disturbance to the existing forests, improving the forest age structure and better managing rocky desertification.
Effects of PBr 3 Addition to Polysilane Thin Films on Structures and Photovoltaic Properties

Organic thin film solar cells are potential next generation solar cells. Many p-type semiconductors have been used in organic solar cells, but there have been far fewer reports involving n-type organic semiconductors. Developing new n-type organic semiconductors is therefore desirable. Polysilane thin films were spin-coated from solutions containing phosphorus (P), and the effects of P addition on film microstructures and electronic properties as n-type semiconductors were investigated. Microstructures of poly-methyl-phenyl-silane (PMPS), dimethyl-polysilane (DMPS) and deca-phenyl-penta-silane (DPPS) thin films were investigated by X-ray diffraction and transmission electron microscopy. PMPS thin films doped with P (PMPS(P)) were amorphous upon annealing at 300°C, while DMPS(P) and DPPS(P) thin films exhibited crystalline structures. PMPS(P) and DMPS(P) thin films exhibited decreased electrical resistances upon P doping. The energy gaps of PMPS(P), DMPS(P) and DPPS(P) were 3.5, 3.9 and 3.8 eV, respectively. Decreased photoluminescence intensities of PMPS, DMPS and DPPS were observed upon P doping. The desorption of phenyl and methyl groups from PMPS(P) thin films was observed from Raman scattering measurements. Solar cells containing polysilane(P)/poly[3-hexylthiophene] heterojunction structures were fabricated and exhibited photovoltaic behavior.
An Optical Intense 2D Electric Field Sensor Using a Single LiNO 3 Crystal Current Optics and Photonics

Based on the linear electro-optic (EO) effect of lithium niobite (LiNbO 3 , LN) crystal, an intense two-dimensional (2D) electric field sensor was analyzed, fabricated and experimentally demonstrated. The linear polarized light beam transmits along the optical axis ( z -axis) of the LN crystal, and the polarization direction of the polarized light is 45° to the y -axis. The sensor can detect the intensity of a 2D electric field that is perpendicular to the z -axis. Experimental results demonstrated that the minimum detectable electric field of the sensor is 10.5 kV/m. The maximum detected electric field of the sensor is larger than 178.9 kV/m. The sensitivity of the sensor is 0.444 mV/(kV·m −1 ). The variation of the sensitivity is within ±0.16 dB when the sensor is rotated around a z -axis from 0° to 360°. The variation of the sensor output optical power is within ±1.4 dB during temperature change from 19 ℃ to 26 ℃ in a day (from 7:00 AM to 23:00 PM) and temperature change from 0 ℃ to 40 ℃ in a controllable temperature chamber. All theoretical and experimental results revealed that the fabricated sensor provides technology for the direct detection of intense 2D electric fields.
Effect of laser irradiation on the biochemical changes in seeds and the accumulation of dry matter in the faba bean

of the Institute of Soil Science and Plant Cultivation in Pu3awy. The aim of the studies was the evaluation of some biochemical and physiological changes in faba bean (Vicia faba minor) seeds and the assessment of the dynamics of the accumulation of dry matter in plants grown from seed treated with laser light before sowing. Irradiation of faba bean seeds of the variety Nadwiœlanski, significantly affected the activity of amylolitic enzymes in the seeds, especially in the germination initial period. Both the threeand five-fold treatment increased enzyme activity to the same degree. It was found that there was a significant effect of seed biostimulation on the scale and rate of dry matter accumulation of particular faba bean organs; the three-fold dose led to an increase in the dynamics in the aboveground part, whereas the five-fold dose, to that in the roots. The weight of vegetative organs intensively reached the phase of faba bean flowering; the highest increase of the total aboveground part of the weight was noted during flowering and pod setting, and was followed by a very fast increase in the weight of the generative organs. Irradiation of the seeds significantly influenced plant germination and modified the course of particular development stages of the faba bean resulting in the accelerated germination and maturity of the plants. K e y w o r d s: faba bean, enzymes activity, laser light, biostimulation, yield
Data Augmentation for EMG-Based Finger Movement Detection

Electromyography (EMG) is an electrodiagnostic medical technique that allows for the recording and analysis of electrical activity within a person’s muscular system. Through pattern recognition of such electrical signals during motion, experts can isolate medical abnormalities or motion patterns. A consequence of this is that, if isolating the muscles associated with finger motion, EMG signals can be used to predict the motion of specific fingers. Identification of finger motion is an important classification problem that falls under the umbrella of human activity recognition. Such practices have many applications and will be thoroughly investigated in the upcoming decades. For example, if an individual loses an extremity such as a hand, EMG classification can be implemented in the replacement prosthetic. Additionally, cybersecurity protocols benefit from such classification technologies as well. Utilizing individualized hand gesture signals could potentially see use-cases in the future of cybersecurity. The applications are limitless and it is important to streamline and improve the EMG classification process. However, EMG classification does have issues that make such tasks difficult. The data is often noisy and potentially limited in size. Previous research has found that computing time-series features from each sequence is sufficient to create input for a machine learning model. While this helps deal with the noisy input, it does raise the time overhead in model fitting. This thesis will tackle this problem by directly utilizing the time-series themselves as an input, as opposed to computed feature vectors. Additionally, this thesis will utilize synthetic data as a data augmentation solution to augment limited training data and balance any potential imbalances in the data. Together, these strategies allow for direct usage of raw time-series sequences as input, and yield high performance in finger movement detection, which demonstrates the effectiveness of the proposed data augmentation strategy.
Effects of Regional Labour Markets on Migration Flows, by Education Level

European economies display large variations in unemployment rates across regions as well as between education groups. Insufficient labour mobility is widely believed to contribute to higher regional disparities and overall unemployment, but few studies have compared mobility responses of different education groups to regional shocks. This paper employs administrative registers covering the entire Norwegian population to compute annual time series from 1994 to 2004 of migration flows and regional labour market conditions by education level for 90 travel‐to‐work areas. We find that regional disparities in unemployment rates are decreasing in education level, whereas the response of migration flows to regional unemployment shocks is increasing in education level. The results suggest that low regional mobility of low‐educated workers may contribute to higher regional disparities and higher overall unemployment among the low educated.
XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection

Explainable AI (XAI) methods are frequently applied to obtain qualitative insights about deep models’ predictions. However, such insights need to be interpreted by a human observer to be useful. In this paper, we aim to use explanations directly to make decisions without human observers. We adopt two gradient-based explanation methods, Integrated Gradients (IG) and backprop, for the task of 3D object detection. Then, we propose a set of quantitative measures, named Explanation Concentration (XC) scores, that can be used for downstream tasks. These scores quantify the concentration of attributions within the boundaries of detected objects. We evaluate the effectiveness of XC scores via the task of distinguishing true positive (TP) and false positive (FP) detected objects in the KITTI and Waymo datasets. The results demonstrate an improvement of more than 100% on both datasets compared to other heuristics such as random guesses and the number of LiDAR points in the bounding box, raising conﬁdence in XC’s potential for application in more use cases. Our results also indicate that computationally expensive XAI methods like IG may not be more valuable when used quantitatively compare to simpler methods.
DEM L241, A SUPERNOVA REMNANT CONTAINING A HIGH-MASS X-RAY BINARY

A Chandra observation of the Large Magellanic Cloud supernova remnant DEM L241 reveals an interior unresolved source which is probably an accretion-powered binary. The optical counterpart is an O5III(f) star making this a high-mass X-ray binary with an orbital period likely to be of the order of tens of days. Emission from the remnant interior is thermal and spectral information is used to derive density and mass of the hot material. Elongation of the remnant is unusual and possible causes of this are discussed. The precursor star probably had mass >25 M☉
Common Variation in the PIN1 Locus Increases the Genetic Risk to Suffer from Sertoli Cell-Only Syndrome

We aimed to analyze the role of the common genetic variants located in the PIN1 locus, a relevant prolyl isomerase required to control the proliferation of spermatogonial stem cells and the integrity of the blood–testis barrier, in the genetic risk of developing male infertility due to a severe spermatogenic failure (SPGF). Genotyping was performed using TaqMan genotyping assays for three PIN1 taggers (rs2287839, rs2233678 and rs62105751). The study cohort included 715 males diagnosed with SPGF and classified as suffering from non-obstructive azoospermia (NOA, n = 505) or severe oligospermia (SO, n = 210), and 1058 controls from the Iberian Peninsula. The allelic frequency differences between cases and controls were analyzed by the means of logistic regression models. A subtype specific genetic association with the subset of NOA patients classified as suffering from the Sertoli cell-only (SCO) syndrome was observed with the minor alleles showing strong risk effects for this subset (ORaddrs2287839 = 1.85 (1.17–2.93), ORaddrs2233678 = 1.62 (1.11–2.36), ORaddrs62105751 = 1.43 (1.06–1.93)). The causal variants were predicted to affect the binding of key transcription factors and to produce an altered PIN1 gene expression and isoform balance. In conclusion, common non-coding single-nucleotide polymorphisms located in PIN1 increase the genetic risk to develop SCO.
Cognitive Modeling of Anticipation: Unsupervised Learning and Symbolic Modeling of Pilots' Mental Representations

The ability to anticipate team members' actions enables joint action towards a common goal. Task knowledge and mental simulation allow for anticipating other agents' actions and for making inferences about their underlying mental representations. In human-AI teams, providing AI agents with anticipatory mechanisms can facilitate collaboration and successful execution of joint action. This paper presents a computational cognitive model demonstrating mental simulation of operators' mental models of a situation and anticipation of their behavior. The work proposes two successive steps: (1) A hierarchical cluster algorithm is applied to recognize patterns of behavior among pilots. These behavioral clusters are used to derive commonalities in situation models from empirical data (N = 13 pilots). (2) An ACT-R (adaptive control of thought - rational) cognitive model is implemented to mentally simulate different possible outcomes of action decisions and timing of a pilot. model tracing of ACT-R allows following up on operators' individual actions. Two models are implemented using the symbolic representations of ACT-R: one simulating normative behavior and the other by simulating individual differences and using subsymbolic learning. Model performance is analyzed by a comparison of both models. Results indicate the improved performance of the individual differences over the normative model and are discussed regarding implications for cognitive assistance capable of anticipating operator behavior.