Miracle-dz/UniFAR-FTUs huggingface dataset:
Insulin Resistance and Level of Antibody to Hepatitis B Surface Insulin Resistance and Level of Antibody to Hepatitis B Surface Antigen in Nondiabetic Healthy Adults Antigen in Nondiabetic Healthy Adults

Background : Hepatitis B virus (HBV) infection may last for a long time and eventually result in liver cirrhosis and hepatocellular cancer (HCC). The best approach for managing HBV infection is primary prevention through vaccination strategies. Anti-HBs concentrations may fall sharply within the ﬁ rst year following the primary HBV immunisation, and fall more slowly after that. The relationship between insulin resistance and Anti-HBs persistence, particularly in a nondiabetic population, has not been examined in any Egyptian studies. Aim of the work : In this study, healthy nondiabetic Egyptian participants who received hepatitis B vaccines were examined to determine the relation between insulin resistance & the level of anti-HBs (post vaccination). Patients and method : There were four groups, 25 participants per each one of them. Groups A (had insulin resistance) and B (had no insulin resistance), were born after 1992 (the year that compulsory baby vaccinations were introduced in Egypt), while groups C (had insulin resistance) and D (had no insulin resistance) were born before 1992. Hepatitis B vaccination (3-dose series on a 0-1-6-month) was administered to these groups (C and D). One to four months following the initial course of three immunization doses, HBs antibody level was determined. Each participant received evaluations for metabolic syndrome, insulin resistance, and obesity. Results : P value of anti-HBs of studied groups A & B was 0.04 and for groups C & D were 0.001. There was one patient of group A, anti-HBs less than 10. P value of fasting insulin of studied groups A & B was 0.001 and groups C & D was 0.001. There is inverse correlation between HBs-Abs titer & HOMA IR and Insulin level. Conclusion : In seemingly healthy Egyptian, nondiabetic men and women, inadvertent loss of anti-HBs over time was correlated with insulin resistance.
Mining and Unifying Heterogeneous Contrastive Relations for Weakly-Supervised Actor-Action Segmentation

We introduce a novel weakly-supervised video actor-action segmentation (VAAS) framework, where only video-level tags are available. Previous VAAS methods follow a synthesize-and-refine scheme, i.e., they first synthesize the pseudo-segmentation and recursively refine the segmentation. However, this process requires significant time costs and heavily relies on the quality of the initial segmentation. Unlike existing works, our method hierarchically mines contrastive relations to supplement each other for learning a visually-plausible segmentation model. Specifically, three contrastive relations are abstracted from the pixel-level and frame-level, i.e., low-level edge-aware, class-activation map aware, and semantic tag-aware relations. Then, the discovered contrastive relations are unified into a universal objective for training the segmentation model, regardless of their heterogeneity. Moreover, we incorporate motion cues and unlabeled samples to increase the discriminative power and robustness of the segmentation model. Extensive experiments indicate that our proposed method produces reasonable segmentation.
Protective Effects of Total Flavonoids from Dracocephalum moldavica L. on Myocardial Ischemia / Reperfusion Injury in the Isolate Rats Heart

The purpose of this study was to examine the cardioprotective effects of total flavonoids from Dracocephalum moldavica L. (DML) on ischemia/reperfusion injury (I/ R) using an isolated Langendorff rat heart preparation. Hearts were perfused with Krebs-Henseleit (K-H) solution containing DML (100 ug/L) for 10 min before 20-minute global ischemia, then reperfusion was begun with K-H solution for 40 min. The left ventricular developed pressure (LVDP) and maximum up/down rate of left ventricular pressure (±dp/dtmax) were recorded by physiological recorder as the myocardial function. And the level of superoxide dismutase (SOD), malondialdehyde (MDA) and GSH/GSSG ratio were analyzed to determine the oxidative stress status in myocardial tissue. Compared with I/R injury group, pre-incubation with DML (100 ug/L) significantly improved LVDP and ±dp/dtmax during reperfusion. Notably, the level of MDA significantly decreased (P<0.01) in DML group compared to I/R injury group, as well as, the SOD activity (P<0.01) and total glutathione (P<0.05) increased. The results implied that the DML has obviously
Northern Greenhouses : an alternative local food provisioning strategy for Nunavik

v ᐃᓱᒪᒋᔭᒃᓴᐅᒥᔪᖅ . vii List of Tables . xv List of Figures . xvii List of Abbreviations and Acronyms . xix Glossary (Definitions of Selected Keywords) . xxi Acknowledgments . xxix Preface . xxxiii Chapter 1. Food in the North . 1 1.1 Food Issues and the Northern Social Context . 2 1.1.1 Food Security in the Canadian North . 2 1.1.2 Introduction to Nunavik and to the Northern Village of Kuujjuaq . 5 1.2 Overview of Inuit Food Provisioning and Consumption Practices . 10 1.2.1 Transformation of Inuit Food Practice during the 19th and 20th Centuries . 10 1.2.2 Main Issues Regarding Food Practice in Nunavik Today . 19 1.2.2.1 Culture and Inuit Food Practice . 19 1.2.2.2 Economics and Inuit Food Practice . 21 1.2.2.3 Health and Inuit Food Practice . 22 1.2.2.4 Natural Environment and Inuit Food Practice . 24 1.2.2.5 Political and Territorial Governance and Inuit Food Practice . 25 Chapter 2. Agriculture in the North . 31 2.1 Overview of Agriculture in the Circumpolar North . 31 2.2 Agricultural Initiatives in Nunavik . 39 2.3 Examples of Northern and Arctic Greenhouses . 47 2.3. 1 Community Greenhouses . 48 2.3. 1.1 Iqaluit Community Greenhouse . 48 2.3. 1.2 Inuvik Community Greenhouse . 51
What can we learn from Honda Smart Home with high-resolution monitored performance data: A zero-net energy home in California ?

The building sector accounts for approximately 40% of the global primary energy consumption, with residential buildings making up more than half. How to effectively reduce energy consumption and the associated Greenhouse Gas (GHG) emissions in residential buildings has become an essential issue for each country to achieve its carbon neutrality goal. This study analyzes the energy performance of the Honda Smart Home (HSH), a two-story, single-family zero-net-energy (ZNE) building situated in northern California. With a comprehensive dataset of 237 channels of one-minute resolution spanning six years, the case study evaluates the ZNE performance at different temporal scales and assesses its energy flexibility and self-sufficiency. Results show a decreasing trend in energy consumption and demonstrate the potential for achieving self-sufficiency and grid-independency. These findings offer insights into the design and operation of future ZNE homes and contribute to the development of the field.
Navigating Investment Waters: Uncovering the Impact of Financial Factors on Business Behavior in Small Economies

Using companies listed on the Nasdaq Baltic Stock Exchange (2017–2021), we analyze the influence of financial factors on the investment behavior of businesses in small economies. We run a panel regression to examine which financial factors influence corporate investment and study how business investment is affected by the previous year’s cash flow, leverage ratio, asset turnover, liquidity, and sales volume. Our findings reveal robust evidence that the level of the investment of firms is negatively affected by the leverage ratio and positively affected by the asset turnover ratio. We also find that the cash flow to asset ratio has a negative but statistically insignificant affection investment. This implies that businesses in small economies are not interested in the capital market. Companies should consider taking the leverage ratio into account and incorporating it in their investment decision-making models.
The Great Lakes Entomologist The Great Lakes Entomologist

It is relatively unknown which species of bees utilize pipeline Right of Ways. Most recent Right of Way research focuses on bees found on electrical transmission Right of Ways instead. Ohio is also relatively understudied in terms of bee richness. This project sought to document bees that are found in these pipeline Right of Ways in 4 heavily forested counties in eastern Ohio. A total of 3950 specimens were collected, accounting for 140 species across two years of sampling (2016 and 2018). Overall bee abundance peaked mid-season, with the highest abundance observed in July. The majority of bees collected were solitary, floral generalist species. The most abundant species, Augochlora pura (Say), accounted for over 20% of all specimens collected. The other most common species were Apis mellifera Linnaeus , Augochlorella aurata (Smith) , Bombus impatiens Cresson , Ceratina calcarata Robertson , Ceratina mikmaqi Rehan and Sheffield , Halictus ligatus Say , Lasioglossum cressonii (Robertson), Lasioglossum coriaceum (Smith) , and Lasioglossum versatum (Rob-ertson). Eight bee species were not native to Ohio, though the most common non-native species was the human reared Western Honey Bee, A. mellifera . Nine bee species were also cleptoparasites of other bees, though overall cleptoparasite numbers were low, accounting for only 15 specimens. Several uncommon specialist bees were also documented including Cemolobus ipomoeae (Robertson) , and Osmia texana Cresson . Overall, this study serves as a baseline for understanding the species of bees that occur in managed pipeline Right of Ways in eastern Ohio.
The Great Lakes Entomologist The Great Lakes Entomologist

villosus as discussed in Gosling (1978, 1981), given that (1) all infested twigs in the current study were living at the time of oviposition, and (2) several of the adults reared in the present study were all confirmed as A. parallelus by David C. L. Gosling, with some
Resolving Ambiguities in LLM-enabled Human-Robot Collaboration

: Large Language Models demonstrate exciting reasoning capabilities that can be utilized in translating user instructions to robot actions in Human-Robot collaboration context. Yet this approach is still prone to failure due to ambiguities in user instruction or interpretation of these instructions in the process of generating actions for a robot. In this extended abstract, we summarize recent work on programming robots through natural language, identify a key research gap, and propose directions for future work with the aim of resolving ambiguities to robustly interpret and clarify natural language instructions.
Journal Pre-proof Feasibility and usefulness of brain imaging in catatonia

Catatonia is a well characterized psychomotor syndrome that has recognizable motor, affective, behavioural and vegetative manifestations. Despite recent demonstration that catatonia is often associated with brain imaging abnormalities, there is currently no consensus or guidelines about the role of brain imaging. In this study, we assessed the feasibility of brain imaging in a series of patients with catatonia in a routine clinical setting and estimated the prevalence of clinically relevant radiological abnormalities. Sixty patients with catatonia were evaluated against sixty non-healthy controls subjects with headache. The MRI reports were reviewed, and MRI scans were also interpreted by neuroradiologists using a standardised MRI assessment. In this cohort, more than 85% of brain scans of patients with catatonia revealed abnormalities. The most frequently reported abnormalities in the catatonic group were white matter abnormalities (n = 44), followed by brain atrophy (n = 27). There was no evidence for significant differences in the frequency of abnormalities found in radiology reports and standardised neuroradiological assessments. The frequency of abnormalities was similar to that found in a population of non-healthy controls subjects with headache. This study shows that MRI is feasible in patients with catatonia and that brain imaging abnormalities are common findings in these patients. Most frequently, white matter abnormalities and diffuse brain atrophy are observed.
A Potential Link Between Waterfall Recession Rate and Bedrock Channel Concavity

The incision of bedrock channels is typically modeled through the stream power or the shear stress applied on the channel bed. However, this approach is not valid for quasi‐vertical knickpoints (hereafter waterfalls), where water and sediments do not apply direct force on the vertical face. As such, waterfall retreat rate is often modeled as a power function of drainage area (a surrogate for hydraulic processes, such as plunge‐pool drilling and groundwater seepage, although a variety of non‐hydraulic processes may also influence waterfall retreat). These different incision modes are associated with two measurable exponents: the channel concavity, θ, that is measured from the channel topography and is used to evaluate the exponents of drainage area and slope in the channel incision model, and p that is measured from the location of waterfalls within watersheds and evaluates the dependency of the waterfall recession rate on drainage area. To better understand the relations between channel incision and waterfall recession, we systematically compare between the exponents p and θ. These parameters were computed from digital elevation models (30‐m Shuttle Radar Topography Mission) of 12 river basins with easily detectable waterfalls. We show that p and θ are (1) similar within uncertainty, (2) come from a similar distribution, and (3) covary for networks with a large number of waterfalls ( ≳10 ). In the context of bedrock incision models this hints that the same processes govern waterfall retreat rate and the incision of nonvertical channel reaches in the analyzed basins and/or that downstream incision can dictate waterfall retreat rate.
Dynamic Programming

Little has been done in the study of these intriguing questions, and I do not wish to give the impression that any extensive set of ideas exists that could be called a "theory. " What is quite surprising, as far as the histories of science and philosophy are concerned, is that the major impetus for the fantastic growth of interest in brain processes, both psychological and physiological, has come from a device, a machine, the digital computer. In dealing with a human being and a human society, we enjoy the luxury of being irrational, illogical, inconsistent, and incomplete, and yet of coping. In operating a computer, we must meet the rigorous requirements for detailed instructions and absolute precision. If we understood the ability of the human mind to make effective decisions when confronted by complexity, uncertainty, and irrationality then we could use computers a million times more effectively than we do. Recognition of this fact has been a motivation for the spurt of research in the field of neurophysiology. The more we study the information processing aspects of the mind, the more perplexed and impressed we become. It will be a very long time before we understand these processes sufficiently to reproduce them. In any case, the mathematician sees hundreds and thousands of formidable new problems in dozens of blossoming areas, puzzles galore, and challenges to his heart's content. He may never resolve some of these, but he will never be bored. What more can he ask?
A Comprehensive Formalization of Propositional Logic in Coq: Deduction Systems, Meta-Theorems, and Automation Tactics

: The increasing signiﬁcance of theorem proving-based formalization in mathematics and computer science highlights the necessity for formalizing foundational mathematical theories. In this work, we employ the Coq interactive theorem prover to methodically formalize the language, semantics, and syntax of propositional logic, a fundamental aspect of mathematical reasoning and proof construction. We construct four Hilbert-style axiom systems and a natural deduction system for propositional logic, and establish their equivalences through meticulous proofs. Moreover, we provide formal proofs for essential meta-theorems in propositional logic, including the Deduction Theorem, Soundness Theorem, Completeness Theorem, and Compactness Theorem. Importantly, we present an exhaustive formal proof of the Completeness Theorem in this paper. To bolster the proof of the Completeness Theorem, we also formalize concepts related to mappings and countability, and deliver a formal proof of the Cantor–Bernstein–Schröder theorem. Additionally, we devise automated Coq tactics explicitly designed for the propositional logic inference system delineated in this study, enabling the automatic veriﬁcation of all tautologies, all internal theorems, and the majority of syntactic and semantic inferences within the system. This research contributes a versatile and reusable Coq library for propositional logic, presenting a solid foundation for numerous applications in mathematics, such as the accurate expression and veriﬁcation of properties in software programs and digital circuits. This work holds particular importance in the domains of mathematical formalization, veriﬁcation of software and hardware security, and in enhancing comprehension of the principles of logical reasoning.
Frequency of anterior knee pain following anterior cruciate ligament reconstruction with bone-tendon-bone autograft

Introduction. Anterior cruciate ligament (ACL) injury is a common condition worldwide, and one of the surgical techniques used to treat it is arthroscopic reconstruction with bone-tendon-bone (BTB) autograft. However, this technique can result in complications such as anterior knee pain, but its prevalence in Colombia is currently unknown. Objective. To establish the frequency of anterior knee pain after ACL reconstruction using BTB autograft in patients operated on over a 4-year period at the Hospital San José in Bogotá, Colombia. Methodology. Descriptive study performed in patients diagnosed with ACL injury and taken to ACL reconstruction surgery using BTB autograft between January 2014 and December 2017. A telephone survey and a clinical assessment were performed to characterize pain in patients presenting with this symptom. Data are described using absolute and relative frequencies, as well as medians and ranges. Results
Functional Impact Of 14 Single Nucleotide Polymorphisms Functional Impact Of 14 Single Nucleotide Polymorphisms Causing Missense Mutations Of Human Î±7 Nicotinic Receptor Causing Missense Mutations Of Human Î±7 Nicotinic Receptor

The α 7nicotinic receptor (nAChR) is a major subtype of the nAChRs in the central nervous system, and the receptor plays an important role in brain function. In the dbSNP database, there are 55 single nucleotide polymorphisms (SNPs) that cause missense mutations of the human α 7nAChR in the coding region. In this study, we tested the impact of 14 SNPs that cause missense mutations in the agonist binding site or the coupling region between binding site and channel gate on the receptor function. The wild type or mutant receptors were expressed or co-expressed in Xenopus oocytes, and the agonist-induced currents were tested using two-electrode voltage clamp. Our results demonstrated that 6 mutants
The Relationship between Mitochondrial Genome Mutations in Monocytes and the Development of Obesity and Coronary Heart Disease.

BACKGROUND Metabolic disorders, including obesity, are often accompanied by an increased risk of cardiovascular complications. Monocytes are the common link between obesity and cardiovascular diseases (CVDs). The bias of innate cellular immunity towards pro-inflammatory activation stimulates the development of diseases associated with chronic inflammation, in particular metabolic disorders, including obesity, as well as CVDs. Disorders in the functional state of monocytes and activation of inflammation may be associated with mitochondrial dysfunction. Mutations accumulating in mitochondrial DNA with age may lead to mitochondrial dysfunction and may be considered a potential marker for developing chronic inflammatory diseases. METHODS The present study aimed to study the relationship between mitochondrial heteroplasmy in CD14+ monocytes and cardiovascular risk factors in 22 patients with obesity and coronary heart disease (CHD) by comparing them to 22 healthy subjects. RESULTS It was found that single-nucleotide variations (SNV) A11467G have a negative correlation with total cholesterol (r = -0.82, p < 0.05), low density lipoproteins (LDL) (r = -0.82, p < 0.05), with age (r = -0.57, p < 0.05) and with mean carotid intima-media thickness (cIMT) (r = -0.43, p < 0.05) and a positive correlation with HDL level (r = 0.71, p < 0.05). SNV 576insC positively correlated with body mass index (BMI) (r = 0.60, p < 0.001) and LDL level (r = 0.43, p < 0.05). SNV A1811G positively correlated with mean cIMT (r = 0.60, p < 0.05). CONCLUSIONS It was revealed that some variants of mitochondrial DNA (mtDNA) heteroplasmy are associated with CVD risk factors. The results demonstrate the potential for using these molecular genetic markers to develop personalized CVD and metabolic disorder treatments.
Fine-Tuning Self-Supervised Models for Language Identification Using Orthonormal Constraint

Self-supervised models trained with high linguistic diversity, such as the XLS-R model, can be effectively fine-tuned for the language recognition task. Typically, a back-end classifier followed by statistics pooling layer are added during training. Commonly used back-end classifiers require a large number of parameters to be trained, which is not ideal in limited data conditions. In this work, we explore smaller parameter back-ends using factorized Time Delay Neural Network (TDNN-F). The TDNN-F architecture is also integrated into Emphasized Channel Attention, Propagation and Aggregation-TDNN (ECAPA-TDNN) models, termed ECAPA-TDNN-F, reducing the number of parameters by 30 to 50% absolute, with competitive accuracies and no change in minimum cost. The results show that the ECAPA-TDNN-F can be extended to tasks where ECAPA-TDNN is suitable. We also test the effectiveness of a linear classifier and a variant, the Orthonor-mal linear classifier, previously used in x-vector type systems. The models are trained with NIST LRE17 data and evaluated on NIST LRE17, LRE22 and the ATCO2 LID datasets. Both linear classifiers outperform conventional back-ends with improvements in accuracy between 0.9% and 9.1%.
Single-shot pixel super-resolution phase imaging by wavefront separation approach

: We propose a novel approach for lensless single-shot phase retrieval, which provides pixel super-resolution phase imaging. The approach is based on a computational separation of carrying and object wavefronts. The imaging task is to reconstruct the object wavefront, while the carrying wavefront corrects the discrepancies between the computational model and physical elements of an optical system. To reconstruct the carrying wavefront, we do two preliminary tests as system calibration without an object. Essential for phase retrieval noise is suppressed by a combination of sparse-and deep learning-based filters. Robustness to discrepancies in computational models and pixel super-resolution of the proposed approach are shown in simulations and physical experiments. We report an experimental computational super-resolution of 2 µ m, which is 3.45 × smaller than the resolution following from the Nyquist-Shannon sampling theorem for the used camera pixel size of 3.45 µ m. For phase bio-imaging, we provide Buccal Epithelial Cells reconstructed with a quality close to the quality of a digital holographic system with a 40 × magnification objective. Furthermore, the single-shot advantage provides a possibility to record dynamic scenes, where the frame rate is limited only by the used camera. We provide amplitude-phase video clip of a moving alive single-celled eukaryote.