Miracle-dz/UniFAR-FTUs huggingface dataset:
Transformer-based deep neural network language models for Alzheimer’s disease detection from targeted speech

Background: We developed transformer-based deep learning models based on natural language processing for early diagnosis of Alzheimer’s disease from the picture description test.Methods: The lack of large datasets poses the most important limitation for using complex models that do not require feature engineering. Transformer-based pre-trained deep language models have recently made a large leap in NLP research and application. These models are pre-trained on available large datasets to understand natural language texts appropriately, and are shown to subsequently perform well on classification tasks with small training sets. The overall classification model is a simple classifier on top of the pre-trained deep language model.Results: The models are evaluated on picture description test transcripts of the Pitt corpus, which contains data of 170 AD patients with 257 interviews and 99 healthy controls with 243 interviews. The large bidirectional encoder representations from transformers (BERTLarge) embedding with logistic regression classifier achieves classification accuracy of 88.08%, which improves thestate-of-the-art by 2.48%. Conclusions: Using pre-trained language models can improve AD prediction. This not only solves the problem of lack of sufficiently large datasets, but also reduces the need for expert-defined features.
Assessment of the predictive role of oestrogen and progesterone receptor status in breast cancer patients treated with neoadjuvant chemotherapy: A retrospective analysis for 689 patients

Background : To explore the predictive indicators in hormone receptor (HR)-positive breast cancer (BC) patients receiving neoadjuvant chemotherapy (NACT) and to evaluate the value of quantitative oestrogen receptor (ER) and progesterone receptor (PR) in predicting tumour response. Methods : Six hundred eighty-nine BC patients with HR-positive status who were treated with anthracycline, epirubicin and taxane NACT treatment were retrospectively analysed. Clinical and pathological features of the patients were used to evaluate the response to NACT. Results : Patients with larger tumour sizes ( OR 1.657 CI 1.186-2.313 p=0.003 ), those who were in a premenopausal status ( OR 1.458 CI 1.039-2.045 p=0.029 ) and those with higher Ki67 levels ( OR 1.735 CI 1.231-2.444 p=0.002 ) exhibited a better therapy response. Among the patients in the postmenopausal subgroup, a lower pretreatment ER or PR expression were associated with a reduction in tumour size, and the cut-off values for ER and PR were 87.5% and 65%, respectively ( p=0.006 and p=0.05 ). Decreased expression of ER and PR was also observed after NACT treatment ( p=0.028 and p<0.001, respectively ) but played only a predictive role in the Her-2-negative subgroup; the cut-off values for decreased ER and PR were 17.5% and 26.5%, respectively ( p=0.044 and p<0.001 ). Conclusions : Semiquantified pretreatment HR expression can be used to predict the response of NACT in postmenopausal BC patients. Decreased ER and PR expression is also associated with a reduction in tumour size in Her-2-negative subtypes treated with NACT.
Incorporation of Li dopant into Cu2ZnSnSe4 photovoltaic absorber: hybrid-functional calculations

We have studied the formation of Li extrinsic defects in Cu2 ? >ZnSnSe4 ? > by first-principles hybrid functional calculations. Li atoms in the Cu site (LiCu ? >) and Li atoms in the Se site (LiSe ? >) are the most and the least stable point defect, respectively. The formation energies of two Li interstitial defects with different numbers of nearest neighbors are the same. These interstitial point defects act as a donor but do not create gap states. Formation of the acceptor point defects (LiZn ? > and LiSn ? >) is less likely in p-type Cu2 ? >ZnSnSe4 ? > compared with n-type Cu2 ? >ZnSnSe4 ? >. In contrast to LiZn ? > which does not create gap states, the formation of LiSn ? > creates two charge transition levels in the middle of the bandgap which might act as recombination centers. (Li–Li)Cu ? > dumbbells are likely to form in p-type Cu2 ? >ZnSnSe4 ? > but the probability of the formation of dumbbells decreases in favor of the formation of two LiCu ? > point defects when the chemical potential of the electrons increases.
Evaluation of some traditional medicinal plants: phytochemical profile, antibacterial and antioxidant potentials

Bioassays provide an authentication of the traditional knowledge of medicinal plants. Antibacterial, antioxidant and phenolic content of nine medicinal plants (Achillea millefolium, Prunella vulgaris, P. laciniata, Lythrum salicaria, Epilobium angustifolium, E. hirsutum, Pedicularis comosa, Agrimonia eupatoria and Verbena officinalis) were investigated in connection with folkloric usages. Aerial parts of nine plant species were extracted with water and methanol (MeOH). Disc diffusion method was performed to evaluate the antibacterial activity of the extracts against ten pathogenic bacteria. Antioxidant activity was determined by using 2,2-diphenyl-1picrylhydrazil (DPPH) radical photometric assay. Total phenolic and flavonoid content were investigated by using Folin-Ciocaltaeu and aluminum chloride (AlCl3) colorimetric method, respectively. Quantitative analysis of phenolic constituents of nine plants species were performed by high performance liquid chromatography-diode array detector (HPLC-DAD) via chosen ten phenolic standards (gallic acid monohydrate, caffeic acid, rutin hydrate, luteolin-7-O-β-D glucoside, kaempferol, rosmarinic acid, myricetin, quercetin, coumarin and apigenin). E. hirsutum showed the best antibacterial activity against gram-positive bacteria (S auerus, S. epidermidis and S. pyogenes). L. salicaria also demonstrated strong and broad spectrum antibacterial activity. P. laciniata, L. salicaria and E. angustifolium showed potent antioxidant activity. The highest phenolic and flavonoid content was observed with E. hirsutum and A. millefolium, respectively. L. salicaria also had very high amount of total phenol and flavonoid content. HPLC-DAD analysis displayed that P. laciniata and A. eupatoria were the best sources of rosmarinic acid. Furthermore, V. officinalis and P. vulgaris also had remarkable amount of rutin. This study revealed the scientific rationale behind the traditional knowledge of the tested plants. Consistent with traditional usages, the most prominent plants were L. salicaria, E. hirsutum and E. angustifolium in regard to strong antibacterial and antioxidant potentials. These plants may be proper natural sources with potential applications in pharmaceutical and food industry.
Learning OWL 2 Property Characteristics as an Explanation for an RNN

We propose an approach to indirectly learn the Web Ontology Language OWL 2 property characteristics as an explanation for a deep recurrent neural network (RNN). The input is a knowledge graph represented in Resource Description Framework (RDF) and the output are scored axioms representing the characteristics. The proposed method is capable of learning all the characteristics included in OWL 2: functional, inverse functional, reflexive and irreflexive, symmetric and asymmetric, transitive. We report and discuss experimental evaluation on DBpedia 2016-10, showing that the proposed approach has advantages over a simple counting baseline.
Personality traits and individual performance: test of the mediating role of motivation among top management

Personality traits, considered as indicators of the employee's performance and motivation which concentrated on intrinsic and extrinsic motivation (Mot), plays an important role in employee performance. This paper aims to investigate the relationships between the personality traits (conscientiousness, neuroticism, extraversion, openness to experience, and agreeableness) and the individual in-role and extra-role performance (IP). In addition, the motivation is being explored in terms of intrinsic and extrinsic of employees. To achieve these objectives, a data-set was collected by surveying 167 general managers (GMs) and general manager's assistants (Asst. GMs) of five star hotels in Malaysia. The partial least squares-structural equation modelling (PLS-SEM) was used to examine the dataset and test the hypotheses. The findings indicated a significant linkage among extraversion, conscientiousness, agreeableness, Mot and IP. On the other hand, Mot mediated the relationships among extraversion, conscientiousness, agreeableness and IP. The participants of the current study revealed a better individual in-role and extra-role performance through an appropriate implementation of personality traits practice.
Efficacy of Dao Yin in patients with 2019 novel coronavirus pneumonia: study protocol for a randomized controlled trial

Background The epidemic of 2019 novel coronavirus (2019-nCoV) pneumonia has become a focused public health event in world. The authorities have taken prompt medical action in China, which contained strict isolation measures and potential symptomatic supportive care. Dao Yin, a traditional Chinese exercise, combines limb movements and breathing exercises. Previous researches showed that it can activate the immune system and improve the lung function and the mental state of patients. Therefore, there may be a potential effect of Dao Yin on the physical and mental health of patients with 2019-nCoV. The aim of this trail is to verify whether conventional therapy plus Dao Yin could show better effects for 2019-nCoV than conventional therapy alone.Methods/design The study is a multicenter randomized controlled trial with parallel-group design including two intervention groups: a conventional therapy group and a conventional therapy plus Dao Yin group. A total of 186 eligible participants will be randomly assigned to the groups in a 1:1 ratio. The routine treatment of the two groups is performed daily according to guideline, and Dao Yin is performed once a day until the patient is out of the hospital. The primary outcome is the Length of Hospital Stay (LHS). Secondary outcomes include the vital signs, respiratory symptoms, questionnaire of mental health and the quantity of immune cells. The outcomes will be assessed at five points including the baseline, 3th, 6th, 9th day during hospitalization and the discharge day. The significance level is 5%. This study will focus on the value of a conventional therapy plus Dao Yin as the treatment for 2019-nCoV and will explore any potential connection among the outcomes.Discussion This study may evaluate the efficacy of conventional therapy plus Dao Yin for 2019-nCoV, which can contribute to provide a solid evidence of Dao Yin therapy for the nationwide emergence.Trial registration: Chinese Clinical Trial Registry, ChiCTR2000029978 Registered on 18 February 2020.
Orientação política e apoio ao isolamento social durante a pandemia da COVID-19: evidências do Brasil

Abstract Social distancing practices have been widely recommended to curb the COVID-19 pandemic. However, despite the medical consensus, many citizens have resisted adhering to and/or supporting its implementation. While this resistance may stem from the non-negligible personal economic costs of implementing social distancing, we argue that it may also reside in more fundamental differences in normative principles and belief systems, as reflected by political orientation. In a study conducted in Brazil, we test the relative importance of these explanations by examining whether and how support for social distancing varies according to self-identified political orientation and personal economic vulnerability. Results show that while economic vulnerability does not influence support for social distancing, conservatives are systematically less supportive of these practices than liberals. Discrepancies in sensitivity to threats to the economic system help explain the phenomenon.
Remarkable O2 permeation through a mixed conducting carbon capture membrane functionalized by atomic layer deposition

The development of energy-efficient and cost-competitive carbon capture technology is of vital importance to effective reduction of carbon emissions and mitigation of global climate change. The present work reports that a highly energy-efficient electrochemical carbon capture membrane consisting of a carbonate and silver phase exhibits a remarkably high rate of O2 permeation after the silver phase is functionalized with a nanoscale layer of Al2O3 by atomic layer deposition. The resulting permeation flux ratio of O2 to CO2 was 1.5:1, a complete reversal from 1:2 of the conventional membrane. The mechanisms of this exceptionally high rate of O2 permeation were investigated by in situ Raman spectroscopy and theoretical DFT calculations. The results revealed that LiCO4− as the active surface species was responsible for the enhanced O2 permeation. A new CO2/O2 transport model based on a “cogwheel” migration mechanism of CO42− was presented to explain the experimental results with excellent agreement.
Novel Manufacturing, Spinning, and Characterization of Polyesters Based on 1,2-Ethanediol and 1,3-Propanediol

Poly(ethylene terephthalate) (PET), poly(trimethylene terephthalate) (PTT), poly(ethylene isophthalate) (PEI), and poly(trimethylene isophthalate) (PTI) were synthesized in a Parr reactor and melt-spun. Thermal and physical properties of the as-synthesized polymers and melt-spun fibers were determined. As-synthesized PEI and PTI were amorphous polymers and did not show any melting peaks by DSC analysis. All the polymers were thermally stable (TGA analysis). Amorphous films were made by a melt-press method with PET and PEI for determination of CO2 gas barrier properties. PEI, which has the meta-linkage of ester groups on the phenyl ring, had much lower CO2 gas permeability around one tenth that of PET, which has the para-linkage of ester groups on the phenyl ring. This is because in PET the phenyl rings are substituted in the para (1,4) positions, which allows for their facile flipping, effectively permitting gases to pass through. However, the meta-substituted phenyl rings in PEI do not permit such ring flipping, and thus PEI may be more suitable for barrier applications. The coalesced PEI was prepared from the inclusion compound of PEI with γ-cyclodextrin. The coalesced PEI may have retained partially highly extended and parallel chains from the narrow channels of the inclusion compound, resulting in better/tighter packing among the PEI chains and exhibited a higher glass-transition temperature. Cyclic oligoesters of PET, PTT, PEI, and PTI were prepared by cyclo-depolymerization of these polyesters. The cyclic oligoesters were mixtures of different sized cyclic oligomers. PET cyclic oligomers showed four melting peaks at 59, 122, 194, and 276 o C. The cyclic oligomers of PTT, PEI, and PTI showed single melting peaks at 241, 335o C and 147o C, respectively. The cyclic oligoesters could be converted to linear polyesters by ring-opening polymerization. PTT was also prepared by ring-opening polymerization of its cyclic dimer obtained as a by-product in the conventional manufacturing plant. Antimony, tin, and titanium catalysts were used with various concentrations. The highest molecular weight, 40,000 g/mol was obtained when 0.25 mol% of titanium(IV) butoxide was used.
Artificial Intelligence Techniques as Potential Tools for Large Scale Surveillance and Interventions for Obesity

Obesity and diabetes are two metabolic disorder diseases, which are strictly correlated. The diagnosis and surveillance of obesity is crucial for public health management, policy making, and interventions. Current practices are mainly based on individuals’ visits to hospitals or clinics to get the measurement and diagnosis for obesity and diabetes, or with telephone calls and personal interviews for surveillance. We advocate that with advances in artificial intelligence (AI), there is great potential to perform obesity diagnosis and surveillance with AI technologies. The key approaches are based on taking pictures or photos of human faces or bodies by using camera sensors, performing computational analysis of the photos, and obtaining the body mass index (BMI) estimation. These AI technologies make it possible to accomplish a large scale diagnosis and monitoring of public health conditions. Furthermore, these technologies also make it possible for interventions with large populations, aided by Internet connections and smart phones for communications. In this article, the aforementioned idea is presented with a brief overview and summary of the currently available AI technologies, opening a window for an innovative way to perform diagnosis, surveillance, and interventions for obesity.
The Swift Surge of Perovskite Photovoltaics

T breakthrough early 1990s dye sensitization of mesoscopic TiO2 films along with a regenerative iodide redox couple led to the explosive growth of dye-sensitized solar cell (DSC) research. The pioneering work of Graẗzel and colleagues also made it possible to develop a solid-state DSSC with spiro-oMETAD as the hole conductor and thus replace the liquid electrolyte in the cell. Research efforts of Konenkamp and others further initiated the search for the “extremely thin absorber” (ETA) nanostructured solar cell, using TiO2 as the electron conductor, an inorganic absorber, and a hole conductor. Another major research thrust was by Weller, Kamat, Zaban, Nozik, Hodes, and others, who employed inorganic quantum dots (e.g., CdS and CdSe) as sensitizers. While discussing developments in sensitized solar cells, it is important to note the contributions of early visionaries like Gerischer, Sutin, and Bard, who were first to establish the concepts of sensitization using dye molecules and semiconductor nanostructures. The efforts to develop solid-state solar cells initiated effervescent activity around 2010 when efficiencies of 5% were attained with Sb2S3 inorganic sensitizers and organic hole conductors. However, the efficiencies of inorganic semiconductor-sensitized solar cells lagged behind those achieved with the liquid junction DSCs. This stalemate took a major leap in late 2012 when a relatively less known organometal halide perovskite, CH3NH3PbI3 (Figure 1), emerged as the light
Can extension programs improve grazing management in rangelands: a case study in Australia

A key challenge in reducing sediment moving from grazing lands into the Great Barrier Reef in Australia is to encourage beef cattle producers to improve management practices. Excessive grazing pressures cause land degradation, leading to both increased sediment runoff and lower future profits. Although higher grazing rates may be possible (and profitable) in better seasons, slow rates of adjustment to poorer seasons can lead to overgrazing and negative impacts on land condition. For policymakers the challenge is to find mechanisms that encourage or signal producers to be more precise in their management and avoid overstocking. Some of the most common options include extension programs, grant programs that use financial incentives, and regulation. In this paper we outline a conceptual framework that shows why extension may be a more powerful driver of management change than incentive programs, and then test this through an evaluation of a case study program conducted with beef cattle producers in catchments of the Great Barrier Reef, Australia. The pathway involving landholders to implement management change was through improved efficiency and productivity, as these are the issues that drive ongoing participation in broader environmental programs. The results present multiple lines of evidence to infer positive outcomes of an extension program in terms of changed management practices, which may be expected to generate improved productivity and better water quality outcomes. These can be grouped into three key areas. First, outcomes show positive improvement relative to the Grazing Water Quality Risk framework for the Great Barrier Reef catchments, which is designed to assess the links between land management and water quality. This indicates that resource condition is likely to improve and sediment emissions should be reduced over time. A second outcome is increased landholder engagement and improved understanding of their business and engagement in future programs, which should underpin ongoing adoption. A third outcome is improved management of risk and developing the skills to do this through data collection and monitoring, which should improve management responses in drought years.
Characterizing Convection Schemes Using Their Responses to Imposed Tendency Perturbations

Convection is usually parameterized in global climate models, and there are often large discrepancies between results obtained with different convection schemes. Conventional methods of comparing convection schemes using observational cases or directly in three‐dimensional (3D) models do not always clearly identify parameterization strengths and weaknesses. In this paper we evaluate the response of parameterizations to various perturbations rather than their behavior under particular strong forcing. We use the linear response function method proposed by Kuang (2010) to compare 12 physical packages in five atmospheric models using single‐column model (SCM) simulations under idealized radiative‐convective equilibrium conditions. The models are forced with anomalous temperature and moisture tendencies. The temperature and moisture departures from equilibrium are compared with published results from a cloud‐resolving model (CRM). Results show that the procedure is capable of isolating the behavior of a convection scheme from other physics schemes. We identify areas of agreement but also substantial differences between convection schemes, some of which can be related to scheme design. Some aspects of the model linear responses are related to their RCE profiles (the relative humidity profile in particular), while others constitute independent diagnostics. All the SCMs show irregularities or discontinuities in behavior that are likely related to threshold‐related mechanisms used in the convection schemes, and which do not appear in the CRM. Our results highlight potential flaws in convection schemes and suggest possible new directions to explore for parameterization evaluation.
Adding a reaction-restoration type transmission rate dynamic-law to the basic SEIR COVID-19 model

The classical SEIR model, being an autonomous system of differential equations, has important limitations when representing a pandemic situation. Particularly, the geometric unimodal shape of the epidemic curve is not what is generally observed. This work introduces the {beta}SEIR model, which adds to the classical SEIR model a differential law to model the variation in the transmission rate. It considers two opposite thrives generally found in a population: first, reaction to disease presence that may be linked to mitigation strategies, which tends to decrease transmission, and second, the urge to return to normal conditions that pulls to restore the initial value of the transmission rate. Our results open a wide spectrum of dynamic variabilities in the curve of new infected, which are justified by reaction and restoration thrives that affect disease transmission over time. Some of these dynamics have been observed in the existing COVID-19 disease data. In particular and to further exemplify the potential the model proposed in this article, we show its capability of capturing the evolution of the number of new confirmed cases of Chile and Italy for several months after epidemic onset, while incorporating a reaction to disease presence with decreasing adherence to mitigation strategies, as well as a seasonal effect on the restoration of the initial transmissibility conditions.