Miracle-dz/UniFAR-FTUs huggingface dataset:
Contribution of Sensory Encoding to Measured Bias

Signal detection theory (SDT) is a widely used theoretical framework that describes how variable sensory signals are integrated with a decision criterion to support perceptual decision-making. SDT provides two key measurements: sensitivity (d′) and bias (c), which reflect the separability of decision variable distributions (signal and noise) and the position of the decision criterion relative to optimal, respectively. Although changes in the subject's decision criterion can be reflected in changes in bias, decision criterion placement is not the sole contributor to measured bias. Indeed, neuronal representations of bias have been observed in sensory areas, suggesting that some changes in bias are because of effects on sensory encoding. To directly test whether the sensory encoding process can influence bias, we optogenetically manipulated neuronal excitability in primary visual cortex (V1) in mice of both sexes during either an orientation discrimination or a contrast detection task. Increasing excitability in V1 significantly decreased behavioral bias, whereas decreasing excitability had the opposite effect. To determine whether this change in bias is consistent with effects on sensory encoding, we made extracellular recordings from V1 neurons in passively viewing mice. Indeed, we found that optogenetic manipulation of excitability shifted the neuronal bias in the same direction as the behavioral bias. Moreover, manipulating the quality of V1 encoding by changing stimulus contrast or interstimulus interval also resulted in consistent changes in both behavioral and neuronal bias. Thus, changes in sensory encoding are sufficient to drive changes in bias measured using SDT. SIGNIFICANCE STATEMENT Perceptual decision-making involves sensory integration followed by application of a cognitive criterion. Using signal detection theory, one can extract features of the underlying decision variables and rule: sensitivity (d′) and bias (c). Because bias is measured as the difference between the optimal and actual criterion, it is sensitive to both the sensory encoding processes and the placement of the decision criterion. Here, we use behavioral and electrophysiological approaches to demonstrate that measures of bias depend on sensory processes. Optogenetic manipulations of V1 in mice bidirectionally affect both behavioral and neuronal measures of bias with little effect on sensitivity. Thus, changes in sensory encoding influence bias, and the absence of changes in sensitivity do not preclude changes in sensory encoding.
Creation of value through a harvester on-board bucking optimization system operated in a spruce stand

Tree bucking, defined as the process in which a stem is segmented into shorter logs of varying lengths, has a significant effect on the value adding potential of a forest enterprise. Because of its importance in terms of correct product and length combinations, improper bucking can lead to financial losses. In this study, two treatments (OFF: quality bucking performed by the operator while using hot keys and ON: automatic bucking using the optimized suggestions from the harvester on-board computer; OBC) were tested in a Norway spruce (Picea abies [L.] Karst.) dominated stand located in Germany. Both treatments had the aim to maximize the value of a stem. The research took place in an 80-year old spruce and beech stand under a regenerative cutting. Fully-mechanized harvesting was performed with an 8-wheel Ponsse Bear single-grip harvester equipped with a H8 harvesting head. Results indicated that the product recovery of the two treatments differed by 4% in undamaged trees (no broken tree-tops or stems) to the benefit of manual bucking. However, the revenue of trees subjected to optimized bucking were up to 4% higher (in average 3%) than those of the manual bucking once expressed on a per cubic meter basis. Moreover, the harvesting productivity of the ON treatment was at the maximum 17% higher compared to the OFF treatment. Based on the results from this case study, the use of an optimization software in Norway spruce dominated stands with the aim to maximize the value of single stems showed promising results.
Robust Trajectory and Beamforming Design for Cognitive MISO UAV Networks

Cognitive radio is a promising technology to tackle the spectrum scarcity problem in unmanned aerial vehicle (UAV) communications. A cognitive multiple input single output (MISO) UAV network is studied under the imperfect user location estimation in this letter. The transmission power is minimized by jointly optimizing the robust trajectory and beamforming of the UAV. An iterative algorithm is proposed to address the challenging problem based on our proposed rank-one theorem. Simulation results demonstrate that our proposed scheme is superior to the benchmark scheme in terms of the transmission power.
Covid-19 Impact on Mental Health of Egyptians Patients

The current study is aimed to examine the rates of mental disorders in a sample of Egyptian patients due to environmental factors associated with the COVID-19 pandemic crisis which interacted with the historical and religious background of the Egyptian people. The research was performed in four different districts in Cairo and Giza before and during the outbreak of a pandemic. The Control group consisted of 1600 patients and extended from the first of October till the end of December 2019. The experimental group consisted of 1200 patients during the first four months of 2020. Mood disorders were the main psychiatric disorders that appeared during this period in the form of depression and anxiety disorders. Also, the cases of substance use disorder (SUD) and obsessive-compulsive disorders (OCD) were presented more in psychiatric clinics. The percentage of male patients was slightly higher in depressive cases, while it was significantly higher in SUD cases. On the other hand, the percentage of female patients was a little higher in anxiety disorders and OCD cases. The percentage of SUD cases was significantly higher among highly educated patients and also in OCD cases. Medium educated patients presented with depression and anxiety disorders were slightly higher than high educated patients and significantly higher than low educated ones.
Variability in the Analgesic Response to Ibuprofen Following Third-Molar Extraction is Associated with Differences in Activation of the Cyclooxygenase Pathway

The analgesic efficacy of non-steroidal anti-inflammatory drugs (NSAIDs) has long been recognized to be limited by substantial interindividual variability in pain relief, but the underlying mechanisms are not well understood. We performed pain phenotyping, functional neuroimaging, pharmacokinetic/pharmacodynamic assessments, inflammation biomarkers, and gene expression profiling in healthy subjects who underwent surgical extraction of bony impacted third molars, in order to characterize factors associated with heterogeneity in response to ibuprofen. Subjects were treated with rapid-acting ibuprofen (400 mg; N=19) or placebo (N=10) in a randomized, double-blind design. Compared to placebo, ibuprofen-treated subjects exhibited greater reduction in pain scores, alterations in regional cerebral blood flow in brain regions associated with pain processing, and inhibition of ex vivo cyclooxygenase activity and urinary prostaglandin metabolite excretion as indices of biochemical drug action (p<0.05). As expected, ibuprofen-treated subjects could be stratified into partial responders (N=9, required rescue medication within the dosing interval) and complete responders (N=10, no rescue medication). This was also reflected by differences in pain scores (p<0.01) as early as 30 minutes following drug administration (p<0.05). Variability in analgesic efficacy was not associated with demographic or clinical characteristics, ibuprofen pharmacokinetics, metabolizing enzyme genotype, or the degree of cyclooxygenase inhibition by ibuprofen. However, complete responders had higher concentrations of inflammatory biomarkers in urine and serum, than partial responders. Specifically, a stable urinary prostaglandin E2 metabolite, serum TNFα and IL-8 were higher in patients who did not require rescue medication compared those who did (p < 0.05). RNAseq gene expression analysis in PBMCs collected after surgery and ibuprofen administration showed enrichment of inflammation related pathways among genes differentially expressed (q < 0.2) between complete and partial responders These findings suggest that patients who receive substantial pain relief from ibuprofen have a more pronounced activation of the prostanoid biosynthetic pathway and regulation of the inflammatory pain phenotype differs from those patients who are insufficiently treated with ibuprofen alone and may require an opioid or other therapeutic intervention.
Dual metabolomic profiling uncovers Toxoplasma manipulation of the host metabolome and the discovery of a novel parasite metabolic capability

The obligate intracellular parasite Toxoplasma gondii is auxotrophic for several key metabolites and must scavenge these from the host. It is unclear how Toxoplasma manipulates host metabolism for its overall growth rate and non-essential metabolites. To address this question, we measured changes in the joint host-parasite metabolome over a time course of infection. Host and parasite transcriptomes were simultaneously generated to determine potential changes in metabolic enzyme levels. Toxoplasma infection increased activity in multiple metabolic pathways, including the tricarboxylic acid cycle, the pentose phosphate pathway, glycolysis, amino acid synthesis, and nucleotide metabolism. Our analysis indicated that changes in some pathways, such as the tricarboxylic acid cycle, derive from the parasite, while changes in others, like the pentose phosphate pathway, were host and parasite driven. Further experiments led to the discovery of a Toxoplasma enzyme, sedoheptulose bisphosphatase, which funnels carbon from glycolysis into ribose synthesis through a energetically driven dephosphorylation reaction. This second route for ribose synthesis resolves a conflict between the Toxoplasma tricarboxylic acid cycle and pentose phosphate pathway, which are both NADP+ dependent. During periods of high energetic and ribose need, the competition for NADP+ could result in lethal redox imbalances. Sedoheptulose bisphosphatase represents a novel step in Toxoplasma central carbon metabolism that allows Toxoplasma to satisfy its ribose demand without using NADP+. Sedoheptulose bisphosphatase is not present in humans, highlighting its potential as a drug target. Author Summary The obligate intracellular parasite Toxoplasma is commonly found among human populations worldwide and poses severe health risks to fetuses and individuals with AIDS. While some treatments are available they are limited in scope. A possible target for new therapies is Toxoplasma’s limited metabolism, which makes it heavily reliant in its host. In this study, we generated a joint host/parasite metabolome to better understand host manipulation by the parasite and to discover unique aspects of Toxoplasma metabolism that could serve as the next generation of drug targets. Metabolomic analysis of Toxoplasma during an infection time course found broad activation of host metabolism by the parasite in both energetic and biosynthetic pathways. We discovered a new Toxoplasma enzyme, sedoheptulose bisphosphatase, which redirects carbon from glycolysis into ribose synthesis. Humans lack sedoheptulose bisphosphatase, making it a potential drug target. The wholesale remodeling of host metabolism for optimal parasite growth is also of interest, although the mechanisms behind this host manipulation must be further studied before therapeutic targets can be identified.
A non-dimensional, two-parameter mechanical model reveals alterations in nuclear mechanics upon Hepatitis C Virus infection

Morphology of the nucleus is an important regulator of gene-expression. Nuclear morphology is in turn a function of the forces acting on it and the mechanical properties of the nuclear envelope. Here, we present a two-parameter, non-dimensional mechanical model of the nucleus that reveals a relationship among nuclear shape parameters such as projected area, surface area and volume. Our model fits the morphology of individual nuclei and predicts the ratio between forces and modulus in each nucleus. We analyzed the changes in nuclear morphology of liver cells due to Hepatitis C Virus (HCV) infection using this model. The model predicted a decrease in the elastic modulus of the nuclear envelope and an increase in the pre-tension in cortical actin as the causes for the change in nuclear morphology. These predictions were validated biomechanically by showing that liver cells expressing HCV proteins possessed enhanced cellular stiffness and reduced nuclear stiffness. Concomitantly, cells expressing HCV proteins showed down-regulation of lamin-A,C and up-regulation of actin, corroborating the predictions of the model. Our modelling assumptions are broadly applicable to adherent, monolayer cell cultures making the model amenable to investigate changes in nuclear mechanics due to other stimuli by merely measuring nuclear morphology. Towards this, we present two techniques, graphical and numerical, to use our model for predicting physical changes in the nucleus.
Clusterin in cancer: Dual role as a tumor suppressor gene and an oncogene

Clusterin (CLU), a heterodimeric and sulfated glycoprotein has been associated with various physiological functions. This molecular chaperone protein is ubiquitously expressed in diverse tissues and conserved across species. Differences in subcellular localization and possible existence of different CLU isoforms may contribute to its functional diversity. Increased or decreased expression of CLU has been observed in several cancers versus normal tissues and hence its role in tumorigenesis is controversial. Evidences from several studies imply that CLU may have a dual role as a tumor suppressor gene or an oncogene depending on the signal and cellular context. CLU possibly exerts its oncogenic role by inhibiting apoptosis, activating autophagy and modulating several signaling pathways like IGF-1/IGFR, EGFR, NF-kB, PI3K/AKT, TGFp and select miRNAs. CLU may exert its tumor suppressive effects by regulating cell cycle and inducing apoptosis. In cancer, loss of heterozygosity (LOH), copy number loss at CLU locus, epigenetic modifications and expression of select miRNAs may lead to the downregulation of CLU. Custirsen (OGX-011), a second generation antisense oligonucleotide that inhibits CLU expression and increases sensitivity of cancer cells to chemotherapeutic drugs, is currently in phase III clinical trials. CLU is an attractive target in several cancers, however for effective targeting, it is essential to know whether it acts as an oncogene or a tumor suppressor gene in a specific tissue/cellular context. The current review attempts to discuss the two contrasting roles of CLU in cancer and associated regulatory mechanisms. This review also sheds light on the complex CLU splice variants, the varied functional attributes supporting the dual roles in cancer and limitations of the CLU research that warrant attention.
Assessment of airway dimensions in skeletal Class I malocclusion patients with various vertical facial patterns: A cephalometric study in a sample of the Saudi population

OBJECTIVE: To compare airway widths among skeletal Class I malocclusion patients with different vertical facial patterns. MATERIAL AND METHODS: A total of 103 lateral cephalograms of skeletal Class I patients (mean age of 20 ± 2.3 years) with no history of orthodontic treatment, trauma, facial syndromes, or nasopharyngeal dysfunction were included. Based on the Frankfort–mandibular plane angle measurements, the sample was divided into three groups: low-angle, high-angle, and normal-angle groups. Upper and lower pharyngeal airway widths were measured as described by McNamara. The analysis of variance test was performed to compare the means of groups. Pairwise comparisons were performed using Tukey's post-hoc test. Differences were considered statistically significant at P < 0.05. RESULTS: ANOVA showed a significant mean difference between the groups for both the upper and lower airway widths with P values of 0.011 and 0.003, respectively. Tukey Pairwise comparisons showed the upper airway width to be significantly narrower in the high-angle group compared to the normal-angle (P = 0.021) and low-angle groups (P = 0.013). Furthermore, the lower airway width in the high angle group was significantly narrower than the normal angle (P = 0.020) and low-angle groups (P = 0.017). There were no statistically significant differences between normal and low angle groups. CONCLUSIONS: The upper and lower pharyngeal widths in the Class I high-angle group were significantly narrower than those in the normal-angle and low-angle groups.
Dramatic Play Affordances of Outdoor Settings for First- and Second-Grade Children with and without Disabilities

Abstract:Dramatic play is crucial to further children's development, including children with disabilities. This study observed dramatic play behaviors among first- and second-grade children with and without disabilities to determine which play settings encouraged children to engage in quality dramatic play. Fifty-three 6-to-8-year-olds with and without disabilities were observed during lunch recess on an inclusive playground. Results suggest that a well-designed inclusive playground reduces barriers for children with disabilities, creating an environment where children with disabilities can engage in similar play behaviors with their typically developing peers.
The Dynamics of COVID-19 Epidemic in Basrah-Second Report

Received: 28.05. 2020 Accepted: 12.06. 2020 Abstract Background: COVID-19 pandemic still expanding across the world and continuing at modest scale in Basrah. Objective: To further elaborate on COVID-19 epidemic in Basrah province and document the changing dynamic of the disease in terms of demographic characteristics, incidence, case fatality, geographical distribution and time trend. Methods: This study covered COVID-19 cases reported in Basrah from March 9 to May 20, 2020. Two sources were used for obtaining data in this study. The first is an excel sheet containing information on 736 cases covering age, gender, residence, travel history, comorbidity and fate, which is obtained from The Department of Public Health in Basrah. The second is the daily numbers of new cases (also 736) obtained from the daily communiqué of the Ministry of Health. Statistical Package for Social Sciences (SPSS-version 20) and Excel programmes were used for statistical analysis. Results: The mean age ± SD was 35.5 ± 18.9years (for males = 35.6 ± 19.0 and for females = 35.5 ± 18.8), the median was 34 years (males = 34 and females = 33). The incidence rate was higher in older age groups, females and three districts (Basrah city, Al-Hartha and Al-Mdaina). The case fatality was within the international figures and significantly predicted by age (older age is associated with high fatality), travel history and presence of co-morbidity. The epidemic curve is moderate, fluctuant and does not help yet in predicting an exit from the epidemic. Conclusion: The epidemic of COVID-19 in Basrah is moderate, cluster in transmission and changing in time but is unpredictable.
An Exploration of Student Reasoning about Undergraduate Computer Science Concepts: An Active Learning Technique to Address Misconceptions

Computer science (CS) is a popular but often challenging major for undergraduates. As the importance of computing in the US and world economies continues to grow, the demand for successful CS majors grows accordingly. However, retention rates are low, particularly for underrepresented groups such as women and racial minorities. Computing education researchers have begun to investigate causes and explore interventions to improve the success of CS students, from K-12 through higher education. In the undergraduate CS context, for example; student difficulties with pointers, functions, loops, and control flow have been observed. We and others have utilized student responses to multiple choice questions aimed at determining misconceptions, engaged in retroactive examination of code samples and design artifacts, and conducted interviews in an attempt to understand the nature of these problems. Interventions to address these problems often apply evidenced-based active learning techniques in CS classrooms as a way to engage students and improve learning. In this work, I employ a human-centered approach, one in which the focus of data collection is on the student thought processes as evidenced in their speech and writing. I seek to determine what students are thinking not only through what can be surmised in retrospect from the artifacts they create, but also to gain insight into their thoughts as they engage in the design, implementation, and analysis of those artifacts and as they reflect on those processes and artifacts shortly after. For my dissertation work, I have conducted four studies: 1. a conceptual assessment survey asking students to “Please explain your reasoning” after each answer to code tracing/execution questions followed by task-based interviews with a smaller, different group of students 2. a “coding in the wild” think aloud study that recorded the screen and audio of students as
Deep learning for land cover classification and environmental analysis using high-resolution remote sensing data

Deep Learning (DL) has become a breakthrough technology in machine learning, and opportunities are emerging for applications in the Earth Observation Science. Originally, DL algorithms were developed for computer vision problems, and the feasibility of these models needs to be explored for remote sensing topics, such as land cover mapping. Most DL studies are focused on urban mapping or a single scene, and the classification framework needs to be discussed for multiple-image, large-area implementation using high spatial resolution data. In this dissertation, three studies were conducted to explore DL algorithms in different contexts: (i) development of new multi-scale object-based convolutional neural network (multi-OCNN) for large-area land cover classification at 1-m spatial resolution; (ii) evaluation of the deep neural network (DNN) and WorldView-3 image for small wetland mapping; and (iii) mapping of structural conservation practices in Midwest U.S. cropland areas using semantic segmentation method. The research gaps of these studies are further explained in each chapter. In the first study, our findings show that combination of image segmentation, object analysis, and multiscale CNNs in the new multiOCNN method achieved higher accuracy and faster classification compared to those results from pixel-based CNN and fixed-OCNN. The object analysis allows the selection of convolutional locations and appropriate input window for CNN prediction, which reduces the number of predictions per object and improves the spatial agreement of object and input patch size. Finally, the results show that multi-OCNN approach is a practical alternative for large-area application using traditional CNNs. In the second study, a pixel-based DNN classification produced a finedetail mapping of wetlands in the Millrace Flats Wildlife Management Area, Iowa, USA. Our results show that DNN model achieved a good classification performance (0.933) in such a complex area, and the results are quite similar to other machine learning methods (random forest,