Miracle-dz/UniFAR-FTUs huggingface dataset:
BRAZIL’S COMPETITIVENESS IN EXPORTATION OF FOREST PRODUCTS FROM 2008 TO 2018

ABSTRACT This article aims to analyze Brazil’s global competitiveness in the exportation of forest products from 2008 to 2018. Data on importation and exportation were obtained from the Food and Agriculture Organization of the United Nations - FAO, while Revealed Comparative Advantage (RCA), Contribution to Trade Balance (CTB), and Terms of Trade (TOT) were the indicators used to measure international trade. The main results show that Brazil has a positive trade balance for forest products, mainly for wood pulp exports. Brazil’s RCA for forest products was classified as moderate to strong. As for the forest product groups, it was strong for wood pulp, weak to moderate for wood panels, weak for paper and paperboard, weak to nonexpressive for timber, and non-expressive for industrial roundwood and wood fuel. Based on Brazil’s CTB, all the forest product groups have a comparative advantage, except wood fuel. Brazil’s TOT was strong for wood panels, timber, industrial roundwood, wood pulp, in order from most to least expressive. In conclusion, Brazilian exports have comparative advantages and are competitive in the international market.
A Social Innovation Model for Sustainable Development: A Case Study of a Malaysian Entrepreneur Cooperative (KOKULAC)

In Malaysia, social innovation programs are fully supported by the government. However, reports and findings on the related matter have yet to be comprehensively collected due to the current interest. Therefore, the aim of this paper was to provide a better insight and understanding on how social innovation projects could assist in achieving the SDG agenda. Since social innovation is emerging and has just recently been embraced in Malaysia, we present the study as a case based on an entrepreneur cooperative, named KOKULAC, with a grounded theory analysis as a core approach. The findings suggest that there are five sustainability development goals that are very closely related to KOKULAC’s agenda. These goals are no poverty, zero hunger, decent work and economic growth, responsible consumption and production, and partnership for the goals. In addition, the case also presented how the values of sustainable development spur greater social benefits of shared prosperity. We conclude that the proposed model contributes to the expansion of innovation capability theory by shaping the innovations within the scope of social needs, which could be applied in other settings. For future research, we recommend a higher integration between the social innovation model and dynamic capabilities of the networking structures.
Hybrid Exercise Program for Sarcopenia in Older Adults: The Effectiveness of Explainable Artificial Intelligence-Based Clinical Assistance in Assessing Skeletal Muscle Area

Background: Sarcopenia is a geriatric syndrome characterized by decreased skeletal muscle mass and function with age. It is well-established that resistance exercise and Yi Jin Jing improve the skeletal muscle mass of older adults with sarcopenia. Accordingly, we designed an exercise program incorporating resistance exercise and Yi Jin Jing to increase skeletal muscle mass and reverse sarcopenia in older adults. Additionally, machine learning simulations were used to predict the sarcopenia status after the intervention. Method: This randomized controlled trial assessed the effects of sarcopenia in older adults. For 24 weeks, 90 older adults with sarcopenia were divided into intervention groups, including the Yi Jin Jing and resistance training group (YR, n = 30), the resistance training group (RT, n = 30), and the control group (CG, n = 30). Computed tomography (CT) scans of the abdomen were used to quantify the skeletal muscle cross-sectional area at the third lumbar vertebra (L3 SMA). Participants’ age, body mass, stature, and BMI characteristics were analyzed by one-way ANOVA and the chi-squared test for categorical data. This study explored the improvement effect of three interventions on participants’ L3 SMA, skeletal muscle density at the third lumbar vertebra (L3 SMD), skeletal muscle interstitial fat area at the third lumbar vertebra region of interest (L3 SMFA), skeletal muscle interstitial fat density at the third lumbar vertebra (L3 SMFD), relative skeletal muscle mass index (RSMI), muscle fat infiltration (MFI), and handgrip strength. Experimental data were analyzed using two-way repeated-measures ANOVA. Eleven machine learning models were trained and tested 100 times to assess the model’s performance in predicting whether sarcopenia could be reversed following the intervention. Results: There was a significant interaction in L3 SMA (p < 0.05), RSMI (p < 0.05), MFI (p < 0.05), and handgrip strength (p < 0.05). After the intervention, participants in the YR and RT groups showed significant improvements in L3 SMA, RSMI, and handgrip strength. Post hoc tests showed that the YR group (p < 0.05) yielded significantly better L3 SMA and RSMI than the RT group (p < 0.05) and CG group (p < 0.05) after the intervention. Compared with other models, the stacking model exhibits the best performance in terms of accuracy (85.7%) and F1 (75.3%). Conclusion: One hybrid exercise program with Yi Jin Jing and resistance exercise training can improve skeletal muscle area among older adults with sarcopenia. Accordingly, it is possible to predict whether sarcopenia can be reversed in older adults based on our stacking model.
Synthesis and Characterization of Coordination Compound [Eu(µ2-OC2H5)(btfa)(NO3)(phen)]2phen with High Luminescence Efficiency

A high-luminescent, blue-light excitable europium(III) coordination complex, [Eu(µ2-OC2H5)(btfa)(NO3)(phen)]2phen (1) {btfa = benzoyl trifluoroacetone, phen = 1,10-phenantroline}, has been synthesized and investigated. The complex was characterized by infrared (IR) and photoluminescence (PL) spectroscopy. The PL emission spectra of powder samples registered in a range of 10.7–300 K exhibit characteristic metal-centered luminescence bands, assigned to internal radiative transitions of the Eu3+ ion, 5D1→7Fj and 5D0→7Fj (j = 0–4). The high-resolution spectrum of the transition 5D0→7F0 shows that it consists of two narrow components, separated by 0.96 meV, which indicates the presence in the matrix of two different sites of the Eu3+ ion. The splitting pattern of 5D0→7Fj (j = 0–4) transitions indicates that europium ions are located in a low-symmetry environment. The absolute quantum yield and the sensitization efficiency were determined to be 49.2% and 89.3%, respectively. The complex can be excited with low-cost lasers at around 405 nm and is attractive for potential applications in optoelectronics and biochemistry.
First report of multiple resistance of goosegrass to herbicides in Brazil

and EPSPs inhibiting herbicides in Brazil. Objective: Evaluate the control of a goosegrass population from Primavera do Leste, MT, Brazil, suspected of resistance to the glyphosate, fenoxaprop-p-ethyl, and haloxyfop-methyl herbicides, using dose-response curves. Methods: The study was carried out in two stages, the first in Brasília, DF, Brazil, and the second in Piracicaba, SP, Brazil. The first stage consisted in the initial evaluation and identification of resistance, which was confirmed in the second. In the first stage, the herbicides glyphosate, fenoxaprop-p-ethyl, and haloxyfop-methyl were applied to susceptible and resistant goosegrass populations (F1 and F2 generations) with plants with 2 and 3 tillers, using nine rates (0, 0.25, 0.5, 1, 2, 4, 8, 16, and 32 times the recommended rate). In the second stage, the herbicides were applied to susceptible and resistant populations with plants with 4 leaves, using 12 rates (0, 0.03125, 0.0625, 0.125, 0.25, 0.5, 1, 2, 4, 8, 16, and 32 times the recommended rate). Results: The high sensitivity of susceptible plants combined with the high resistance level of resistant ones resulted in high resistance factors for fenoxaprop-p-ethyl and haloxyfop-methyl, with values greater than 27.8. The resistance factor for glyphosate ranged from 3.3 to 11, depending on growth stage at application time. Conclusions: The goosegrass population from Primavera do Leste, MT, Brazil, presented resistance to EPSPs (glyphosate) and ACCase (haloxyfop-methyl and fenoxaprop-ethyl) inhibiting herbicides.
Cinnamaldehyde and Curcumin Prime Akt2 for Insulin-Stimulated Activation

In this study, the effects of cinnamaldehyde and curcumin on Akt2, a serine/threonine protein kinase central to the insulin signaling pathway, were examined in preadipocytes. Cinnamaldehyde or curcumin treatment increased Akt2 phosphorylation at multiple sites including T450 and Y475, but had no effect on Akt2 phosphorylation at S474, which is critical for Akt2 activation. Surprisingly, insulin treatment with cinnamaldehyde or curcumin increased p-Akt2 (S474) by 3.5-fold versus insulin treatment alone. Furthermore, combined cinnamaldehyde, curcumin, and insulin treatment increased p-Akt2 (S474) by 7-fold versus insulin treatment alone. Interestingly, cinnamaldehyde and curcumin inhibited both serine/threonine phosphatase 2A (PP2A) and protein tyrosine phosphatase 1B (PTP1B). Akt2 activation is a multistep process that requires phosphorylation at T450 for proper folding and maturation, and phosphorylation of both Y475 and S474 for stabilization of the catalytic domain. It is plausible that by inhibiting PP2A and PTP1B, cinnamaldehyde and curcumin increase phosphorylation at T450 and Y475, and prime Akt2 for insulin-stimulated phosphorylation at S474. Notably, the combination of a PP2A inhibitor, okadaic acid, and a PTP1B inhibitor increased p-Akt2 (S474), even in the absence of insulin. Future combinations of PP2A and PTP1B inhibitors provide a rational platform to engineer new therapeutics for insulin resistance syndrome.
Weakly Supervised Region of Interest Extraction Based on Uncertainty-Aware Self-Refinement Learning for Remote Sensing Images

Region of interest (ROI) extraction plays a significant role in the field of remote sensing image (RSI) processing. Recently, weakly supervised ROI extraction methods have attracted considerable attention due to low labeling cost. Most of them follow the pipeline of first generating pseudo labels and then using the pseudo labels to train a segmentation model. However, there remain problems to be solved: 1) the unbalanced distribution of foreground and background samples in the RSI dataset influences the network performance; 2) the pseudo labels mainly cover the most discriminative part of object regions that are incomplete; and 3) training with pseudo labels inevitably causes noise issues that degrade the model performance. To solve these issues, we propose a weakly supervised uncertainty-aware self-refinement learning (UASRL) method, where the initial unbalanced image-level labels are progressively refined to high-quality pixel-level annotations. In the proposed UASRL, we first present a deep generative model combined with self-attention modules to improve the unbalanced distribution in the weakly labeled dataset. Then, we design a confidence-weighted complementary erasing-based weakly supervised method to generate pseudo labels with high integrity. Finally, for training with noisy pseudo labels, we develop an uncertainty-aware joint optimization (UAJO) training strategy to reduce the negative effect caused by noisy labels and further refine pixelwise labels in a coarse-to-accurate manner, which in turn jointly promotes the model’s performance. Extensive experiments on three types of RSI datasets reveal that our proposed method is superior to other competing methods and shows a preferable tradeoff between annotation cost and detection performance.
The Effect of COVID-19 Lockdown on PHQ Depression Screening Scores for High School Athletes

Adolescent behavioral health was in crisis before COVID-19. The shutdown and reopening of in-person learning and extracurricular activities may have worsened this crisis. We examined high school athletes’ depression before and during the pandemic. Data were collected as part of a pilot program incorporating Patient Health Questionnaire (PHQ) screenings during high school sports physicals before the COVID-19 lockdown and three timepoints after. Statistical comparisons were made using logistic regression. A total of 927 individual scores were analyzed: 385 from spring 2020; 145 from fall 2020; 163 from fall 2021; and 234 from spring 2022. Fall 2020 students were 3.7 times more likely to have elevated PHQ-2 scores than spring 2020 students (95% CI = 1.8, 7.6). Fall 2021 and spring 2022 scores did not differ significantly from pre-pandemic, although trends of elevated scores persisted (OR = 1.6; 95% CI = 0.7, 3.5, and OR = 1.2; 95% CI = 0.6, 2.4, respectively). A significant difference in PHQ-9 depression severity classification was detected over time (p < 0.01). Elevated PHQ scores were found after the onset of the COVID-19 pandemic. After the initial peak in fall 2020, scores decreased but did not reach pre-pandemic levels.
Flat Detector CT with Cerebral Pooled Blood Volume Perfusion in the Angiography Suite: From Diagnostics to Treatment Monitoring

C-arm flat-panel detector computed tomographic (CT) imaging in the angiography suite increasingly plays an important part during interventional neuroradiological procedures. In addition to conventional angiographic imaging of blood vessels, flat detector CT (FD CT) imaging allows simultaneous 3D visualization of parenchymal and vascular structures of the brain. Next to imaging of anatomical structures, it is also possible to perform FD CT perfusion imaging of the brain by means of cerebral blood volume (CBV) or pooled blood volume (PBV) mapping during steady state contrast administration. This enables more adequate decision making during interventional neuroradiological procedures, based on real-time insights into brain perfusion on the spot, obviating time consuming and often difficult transportation of the (anesthetized) patient to conventional cross-sectional imaging modalities. In this paper we review the literature about the nature of FD CT PBV mapping in patients and demonstrate its current use for diagnosis and treatment monitoring in interventional neuroradiology.
A Target SAR Image Expansion Method Based on Conditional Wasserstein Deep Convolutional GAN for Automatic Target Recognition

For the automatic target recognition (ATR) based on synthetic aperture radar (SAR) images, enough training data are required to effectively characterize target features and obtain good recognition performance. However, in practical applications, it is difficult to collect sufficient training data. To tackle the limitation, a novel end-to-end expansion method, called conditional Wasserstein deep convolutional generative adversarial network with gradient penalty (CWDCGAN), is proposed to achieve SAR image expansion with specified category. To be specific, the CWDCGAN innovatively designed a generative adversarial network architecture based on convolutional and deconvolution networks to improve the quality of generated images. At the same time, conditional information is introduced to control the categories of generated images, and Wasserstein distance and gradient penalty are used to modify the loss function, which makes the network training more stable. Besides, feature extraction and classifier design in a typical ATR system often rely heavily on subjective expert knowledge, which seriously affects its generalization performance. Therefore, a joint recognition method of Resnet18 and support vector machine (Renset18-SVM) is adopted to improve the generalization capacity and the recognition performance. Experimental results with public measured data show that the CWDCGAN can generate higher quality SAR images, and by feeding expanded data to Renset18-SVM, the recognition accuracy is improved under different proportions of training samples.
The longitudinal trajectory of emotion regulation and associated neural activity in patients with bipolar disorder: A prospective fMRI study

Impaired emotion regulation is a key feature of bipolar disorder (BD) that presents during acute mood episodes and in remission. The neural correlates of voluntary emotion regulation seem to involve deficient prefrontal top‐down regulation already at BD illness onset. However, the trajectory of aberrant neuronal activity during emotion regulation in BD is unclear.
Assessing the Effectiveness of Correlative Ecological Niche Model Temporal Projection through Floristic Data

Simple Summary Climate change is the main threat for conservation in the 21st century. Reliable methodologies and tools for the evaluation of its impact are urgently needed. Correlative ecological niche models (ENMs) are effective tools for predicting the future distribution of species under climate change scenarios. Despite this, many alternative different methods have been proposed, and objective reasons for a proper selection are unclear. Therefore, a comparative study to evaluate the consistency of predictions of the main ENM algorithms was performed. To test the effectiveness of correlative ENM temporal projection, we compared predictions generated using historical data and projected to the modern climate with predictions generated using modern distribution and climate data. In total, 600 case studies were generated, by using 25 Italian endemic plant species, 12 algorithms and 2 alternative sets of environmental variables. As a result, we highlighted the similarity of eight algorithms and the poor performance of four. Abstract Correlative ecological niche modelling (ENM) is a method widely used to study the geographic distribution of species. In recent decades, it has become a leading approach for evaluating the most likely impacts of changing climate. When used to predict future distributions, ENM applications involve transferring models calibrated with modern environmental data to future conditions, usually derived from Global Climate Models (GCMs). The number of algorithms and software packages available to estimate distributions is quite high. To experimentally assess the effectiveness of correlative ENM temporal projection, we evaluated the transferability of models produced using 12 different algorithms on historical and modern data. In particular, we compared predictions generated using historical data and projected to the modern climate (simulating a “future” condition) with predictions generated using modern distribution and climate data. The models produced with the 12 ENM algorithms were evaluated in geographic (range size and coherence of predictions) and environmental space (Schoener’s D index). None of the algorithms shows an overall superior capability to correctly predict future distributions. On the contrary, a few algorithms revealed an inadequate predictive ability. Finally, we provide hints that can be used as guideline to plan further studies based on the adopted general workflow, useful for all studies involving future projections.
Formic Acid Generation from CO2 Reduction by MOF-253 Coordinated Transition Metal Complexes: A Computational Chemistry Perspective

The inclusion of transition metal elements within metal–organic frameworks (MOFs) is considered one of the most promising approaches for enhancing the catalytic capability of MOFs. In this study, MOF-253 containing bipyridine coordination sites is investigated for possible transition metal chelation, and a consequent possible CO2 reduction mechanism in the formation of formic acid. All transition metal elements of the third, fourth and fifth periods except hafnium and the lanthanide series are considered using density functional theory calculations. Two distinct types of CO2 reduction mechanisms are identified: (1) the five-coordination Pd center, which promotes formic acid generation via an intramolecular proton transfer pathway; (2) several four-coordination metal centers, including Mn, Pd, and Pt, which generate formic acid by means of heterolytic hydrogen activation. The MOF-253 environment is found to promote beneficial steric hindrance, and to constrain metal–ligand orientation, which consequently facilitates the formation of formic acid, particularly with the tetrahedral Mn center at high-spin electronic state.
Pharmacological Comparative Characterization of REL-1017 (Esmethadone-HCl) and Other NMDAR Channel Blockers in Human Heterodimeric N-Methyl-D-Aspartate Receptors

Excessive Ca2+ currents via N-methyl-D-aspartate receptors (NMDARs) have been implicated in many disorders. Uncompetitive NMDAR channel blockers are an emerging class of drugs in clinical use for major depressive disorder (MDD) and other neuropsychiatric diseases. The pharmacological characterization of uncompetitive NMDAR blockers in clinical use may improve our understanding of NMDAR function in physiology and pathology. REL-1017 (esmethadone-HCl), a novel uncompetitive NMDAR channel blocker in Phase 3 trials for the treatment of MDD, was characterized together with dextromethorphan, memantine, (±)-ketamine, and MK-801 in cell lines over-expressing NMDAR subtypes using fluorometric imaging plate reader (FLIPR), automated patch-clamp, and manual patch-clamp electrophysiology. In the absence of Mg2+, NMDAR subtypes NR1-2D were most sensitive to low, sub-μM glutamate concentrations in FLIPR experiments. FLIPR Ca2+ determination demonstrated low μM affinity of REL-1017 at NMDARs with minimal subtype preference. In automated and manual patch-clamp electrophysiological experiments, REL-1017 exhibited preference for the NR1-2D NMDAR subtype in the presence of 1 mM Mg2+ and 1 μM L-glutamate. Tau off and trapping characteristics were similar for (±)-ketamine and REL-1017. Results of radioligand binding assays in rat cortical neurons correlated with the estimated affinities obtained in FLIPR assays and in automated and manual patch-clamp assays. In silico studies of NMDARs in closed and open conformation indicate that REL-1017 has a higher preference for docking and undocking the open-channel conformation compared to ketamine. In conclusion, the pharmacological characteristics of REL-1017 at NMDARs, including relatively low affinity at the NMDAR, NR1-2D subtype preference in the presence of 1 mM Mg2+, tau off and degree of trapping similar to (±)-ketamine, and preferential docking and undocking of the open NMDAR, could all be important variables for understanding the rapid-onset antidepressant effects of REL-1017 without psychotomimetic side effects.
Cell Cycle Regulation by Integrin-Mediated Adhesion

Cell cycle and cell adhesion are two interdependent cellular processes regulating each other, reciprocally, in every cell cycle phase. The cell adhesion to the extracellular matrix (ECM) via integrin receptors triggers signaling pathways required for the cell cycle progression; the passage from the G1 to S phase and the completion of cytokinesis are the best-understood events. Growing evidence, however, suggests more adhesion-dependent regulatory aspects of the cell cycle, particularly during G2 to M transition and early mitosis. Conversely, the cell cycle machinery regulates cell adhesion in manners recently shown driven mainly by cyclin-dependent kinase 1 (CDK1). This review summarizes the recent findings regarding the role of integrin-mediated cell adhesion and its downstream signaling components in regulating the cell cycle, emphasizing the cell cycle progression through the G2 and early M phases. Further investigations are required to raise our knowledge about the molecular mechanisms of crosstalk between cell adhesion and the cell cycle in detail.