Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative Image Analysis of Axonal Morphology in In Vivo Model

Quantifying axonal branching is crucial for understanding neural circuit function, developmental and regeneration processes and disease mechanisms. Factors that regulate patterns of axonal arborization and tune neuronal circuits are investigated for their implication in various disorders in brain connectivity. The lack of a reliable and user-friendly method makes the quantitative analysis of axon morphology difficult. Specifically, methods to visualize and quantify the complex axon arborization are challenging to implement and apply practically. Our study was aimed at developing a robust but simple method of quantification that used ImageJ 2D analysis and compared it with Imaris visualization and analysis of 3D images. We used zebrafish fluorescent transgenic lines to perform in vivo imaging of developing motor neuron axons that adequately reflected the complexity of axonal networks. Our new method, developed on ImageJ, is easy and fast, giving access to new information such as collateral distribution along the axonal shaft. This study describes step-by-step procedures that can be easily applied to a variety of organisms and in vitro systems. Our study provides a basis for further exploration of neural circuits to gain new insights into neuronal disorders and potential therapeutic interventions.
Exploring Motivational Beliefs and Learning Approaches among Undergraduates Students

In order to understand how students grow into independent learners depends on their motivational beliefs a survey has been conducted. The variables items questioned were adopted from the Motivated Strategies for Learning Questionnaire (MSLQ), developed by Paul R. Pintrich and Debra J. De Groot. The survey is broken into three sections which are demographic profile, motivational belief and independent learners. The number of 140 correspondents were given a set of questionnaires and they are from various backgrounds undergraduate students. The objectives are to perceive motivational beliefs and strategy use of independent learning among learners and to identify the relationship between motivational belief and independent learners. The data was analyzed by identifying the mean for self-efficacy, intrinsic value, text anxiety, cognitive strategy as well as self-regulation, to determine the correlation between these two sections of self-regulated strategies and motivational beliefs. The result shows that there is an implication of motivational and self-learning towards effective learning and teaching processes. From the finding, it also believed that student learning style and educators' support lead to the student’s motivation in the learning process. This study have practical implications for educators, policymakers, and educational institutions by understanding how students perceive their motivational beliefs and engage in self-regulated learning strategies. This will lead to the development of tailored approaches to enhance academic success and engagement in the Malaysian context. The research has the potential to contribute to the enhancement of academic success
Image-Level Automatic Data Augmentation for Pedestrian Detection

Data augmentation (DA) is a commonly used method to alleviate the problem of detecting occluded pedestrians in crowded scenes. Recently, several dataset-level automatic DA methods have been proposed to search for a set of general DA policies for the entire dataset, which saves a lot of time compared to manually designing DA policies. However, due to the huge differences between each image in pedestrian detection datasets, existing dataset-level augmentation methods cannot automatically adjust DA policies according to the differences between them, which will lead to outlier data and degradation of model performance. Therefore, considering the differences between each image, we propose an image-level automatic DA method that aims to find an optimal DA policy for each image in the dataset according to their respective characteristics. Specifically, we first reformulate the image-level automatic DA method by constructing a search space based on categorical distribution, within which we specify the probability of operations being sampled according to their respective effectiveness so that useful operations can be effectively preserved and useless operations can be suppressed. Subsequently, we design an encoding method to recode the index of images and policies and use the encoded index to closely associate them to achieve a stable matching relationship between images and policies. Finally, a search framework with Bayesian optimization is developed for efficient policy mining. Comprehensive experiments on CrowdHuman and CityPersons datasets show that compared with the commonly used automatic DA method for pedestrian detection, AutoPedestrian, our method takes only 1/14 of the search time, but achieves better detection accuracy. Specifically, we achieve 10.2% MR−2 on the CityPersons reasonable subset and 36.8% MR−2 on the CrowdHuman dataset, outperforming state-of-the-art methods on the CrowdHuman dataset.
Air-Chamber-Based Soft Six-Axis Force/Torque Sensor for Human–Robot Interaction

Soft multiaxis force/torque sensors provide safe and precise force interaction. Capturing the complete degree-of-freedom of force is imperative for accurate force measurement with six-axis force/torque (6-F/T) sensors. However, cross-axis coupling can lead to calibration issues and decreased accuracy. In this instance, developing a soft and accurate six-axis sensor is a challenging task. In this article, a soft air-chamber type 6-F/T sensor with 16-channel barometers is introduced, which is housed in hyper-elastic air chambers made of silicone rubber. Additionally, an effective decoupling method is proposed, based on a rigid-soft hierarchical structure, which reduces the six-axis decoupling problem to two three-axis decoupling problems. Finite element model simulation and experiments demonstrate the compatibility of the proposed approach with reality. The prototype’s sensing performance is quantitatively measured in terms of static load response, dynamic load response, and dynamic response characteristics. It possesses a measuring range of 50 N force and 1 Nm torque, and the average deviation, repeatability, nonlinearity, and hysteresis are 4.9%, 2.7%, 5.8%, and 6.7%, respectively. The results indicate that the prototype exhibits satisfactory sensing performance while maintaining its softness due to the presence of soft air chambers.
A 40-nm Compute-in-Memory Macro With RRAM Addressing IR Drop and Off-State Current

This letter describes an analog current-summing compute-in-memory macro using resistive random-access memory (RRAM). The readout transimpedance amplifiers use offset canceling with differential inputs from added sensing paths for the bitline (BL) and sourceline (SL) to minimize channel-to-channel (ch./ch.) gain error while mitigating IR drop in the BL, SL, and multiplexors (MUXes). The analog-to-digital converters (ADCs) use dynamic offset cancelation to remove ch./ch. ADC intrinsic offset and error due to RRAM off-state current. The 64Kb macro implemented with foundry RRAM in 40-nm CMOS has an area of 0.0263 mm2, ch./ch. gain std. dev. of 1.9%, IR drop per-wordline of 0.004%, and 1.1 V efficiency of 7.8–58.8 TOPS/W.
Detection and identification of leukemic and normal cells using Raman spectroscopy and multivariate statistical analysis

Abstract Raman spectroscopy technique was successfully employed to study the difference between leukemic cells (Jurkat cell line) and normal human bone marrow mononuclear cells using near-infrared laser (785 nm) excitation in this study. Significant differences in Raman spectra from two kinds of cell lines showed special changes in the percentage of biochemical constituents in different cells. The obtained spectral data were used to develop diagnostic algorithms by multivariate statistical analysis, including principal component analysis combined with linear discriminate analysis. The multivariate statistical analysis method for classification between cancer and normal cells has achieved good differentiation with high diagnostic sensitivity (100%), specificity (100%), and accuracy (100%), respectively. These results showed that Raman spectroscopy is a novel, nondestructive method, that provided interesting information about the changes in biochemical properties of cells. Raman spectroscopy combined with multivariate statistical analysis can serve as a potential clinical diagnosis method of leukemia.
Nutraceutical Potential of Grape (Vitis vinifera L.) Seed Oil in Oxidative Stress, Inflammation, Obesity and Metabolic Alterations

Vitis vinifera L. (grapevine) is a perennial plant of the Vitaceae family that is widely used to produce grapes and wines. Grape seed oil is rich in fatty acids such as linoleic acid (65–75%), vitamin E (50 mg), and phytosterols in addition to phenolic compounds, such as catechins (414 mg), epicatechins (130.4 mg), and gallic acid (77 µg), shows promise as a nutritional compound and is outstanding as a therapeutic substance with active properties for health, detected mainly by in vitro studies, as well as some in vivo studies. The benefits of consuming this oil include modulating the expression of antioxidant enzymes, anti-atherosclerotic and anti-inflammatory effects, and protection against oxidative cell damage and some types of cancer. However, experimental findings confirm that therapeutic functions remain scarce; thus, more studies are needed to determine the mechanisms of action involved in the indicated therapeutic qualities.
The Integration of the Metabolome and Transcriptome for Dendrobium nobile Lindl. in Response to Methyl Jasmonate

Dendrobium nobile Lindl. , as an endangered medicinal plant within the genus Dendrobium, is widely distributed in southwestern China and has important ecological and economic value. There are a variety of metabolites with pharmacological activity in D. nobile. The alkaloids and polysaccharides contained within D. nobile are very important active components, which mainly have antiviral, anti-tumor, and immunity improvement effects. However, the changes in the compounds and functional genes of D. nobile induced by methyl jasmonate (MeJA) are not clearly understood. In this study, the metabolome and transcriptome of D. nobile were analyzed after exposure to MeJA. A total of 377 differential metabolites were obtained through data analysis, of which 15 were related to polysaccharide pathways and 35 were related to terpenoids and alkaloids pathways. Additionally, the transcriptome sequencing results identified 3256 differentially expressed genes that were discovered in 11 groups. Compared with the control group, 1346 unigenes were differentially expressed in the samples treated with MeJA for 14 days (TF14). Moreover, the expression levels of differentially expressed genes were also significant at different growth and development stages. According to GO and KEGG annotations, 189 and 99 candidate genes were identified as being involved in terpenoid biosynthesis and polysaccharide biosynthesis, respectively. In addition, the co-expression analysis indicated that 238 and 313 transcription factors (TFs) may contribute to the regulation of terpenoid and polysaccharide biosynthesis, respectively. Through a heat map analysis, fourteen terpenoid synthetase genes, twenty-three cytochrome P450 oxidase genes, eight methyltransferase genes, and six aminotransferase genes were identified that may be related to dendrobine biosynthesis. Among them, one sesquiterpene synthase gene was found to be highly expressed after the treatment with MeJA and was positively correlated with the content of dendrobine. This study provides important and valuable metabolomics and transcriptomic information for the further understanding of D. nobile at the metabolic and molecular levels and provides candidate genes and possible intermediate compounds for the dendrobine biosynthesis pathway, which lays a certain foundation for further research on and application of Dendrobium.
Low Crosstalk Plastic Optical Fiber-Based Dual-Parameter SPR Sensor With Stepped Side-Polished Structure and Differentiated Au-Film Thickness

In this article, a surface plasmon resonance (SPR) sensor based on a stepped side-polished plastic optical fiber (POF) is proposed and demonstrated for simultaneous measurement of refractive index (RI) and temperature. A differentiated Au-film thickness was deposited on the stepped polished region, which can enlarge the wavelength space for the SPR peaks of RI and temperature sensing channels and reduce their crosstalk. The polydimethylsiloxane (PDMS) film was fabricated on the polished region with a deeper polished depth and a thicker Au-film thickness for temperature sensing. The stepped polished structure can obtain a better resonance peak visibility for the temperature sensing. The experimental results show that the RI sensitivity of the sensor is 1759 nm/RIU in the RI range of 1.335–1.37 and the temperature sensitivity is −0.82 nm/°C in the temperature range of 20 °C–80 °C. The probe has the advantages of easy fabrication and simple structure, which has potential application prospect in the field of biochemical sensing.
Concealing Exposed Circuit Features Through a MaxSAT-Based Logic Locking Method

Logic locking has emerged as an effective means of hardware intellectual property protection. Nevertheless, the success of machine learning-based attacks has necessitated the development of new countermeasures. In this brief, we propose a novel approach to multiplexer-based locking that conceals structural and functional features common to all learning-based attacks. By employing a constraint programming approach, we formulate those features as weights for Conjunctive Normal Form (CNF) clauses. We then use Maximum Satisfiability (MaxSAT) solvers to choose locked net pairs in a manner that effectively decreases the correlation between the key bits and the features exploited by attackers. The experimental results demonstrate an 44.19% average reduction in the number of correctly predicted key bits by the learning-based MuxLink attack when compared to the DMUX lock. Furthermore, the prediction accuracy for the constant propagation-based SCOPE attack was consistently below 50%, while the number of undecided bits rose by an average 29.04% in comparison with the DMUX lock.
Safety of omitting nasogastric decompression after esophagectomy: a propensity score-matched study

Background Nasogastric (NG) decompression is routinely performed after esophagectomy. However, whether it aids postoperative recovery is still controversial. This study aimed to assess the effects of NG decompression on postoperative complications after esophagectomy. Methods Data of 1,489 consecutive patients who underwent esophagectomy between January 2019 and December 2020 were retrospectively analyzed. All patients were assigned to two groups based on whether they had undergone NG decompression or not. We conducted a propensity score matching (PSM) analysis to minimize the effect of potential confounders. Results In total, 1,466 patients (including 1,235 patients with NG tubes and 231 without NG tubes) were included in the study, and 219 pairs were successfully matched. After PSM analysis, there was no difference in morbidity and mortality between the two groups. Postoperative hospital stay in the non-NG tube group was shorter than that in the NG tube group (8 vs. 10 days, P<0.001). The incidence of pneumonia and anastomotic leakage showed no significant differences (13.2% vs. 17.8%, P=0.235 for pneumonia; 13.7% vs. 11.0%, P=0.460 for anastomotic leakage). For patients who developed anastomotic leakage after surgery, the leakage developed earlier in the non-NG group (6 vs. 8 days, P=0.033) than in the NG group. However, no significant between-group differences were observed in the postoperative hospital stay and severity of leakage. Conclusions Routine NG decompression may not confer any discernible benefits for patients who have undergone esophagectomy. As such, the omission of this procedure could be considered in postoperative care.
Homeostatic iron regulatory protein drives glioblastoma growth via tumor cell-intrinsic and sex-specific responses

Abstract Background Glioblastoma (GBM) displays alterations in iron that drive proliferation and tumor growth. Iron regulation is complex and involves many regulatory mechanisms, including the homeostatic iron regulator (HFE) gene, which encodes the homeostatic iron regulatory protein. While HFE is upregulated in GBM and correlates with poor survival outcomes, the function of HFE in GBM remains unclear. Methods We interrogated the impact of cell-intrinsic Hfe expression on proliferation and survival of intracranially implanted animals through genetic gain- and loss-of-function approaches in syngeneic mouse glioma models, along with in vivo immune assessments. We also determined the expression of iron-associated genes and their relationship to survival in GBM using public data sets and used transcriptional profiling to identify differentially expressed pathways in control compared to Hfe-knockdown cells. Results Overexpression of Hfe accelerated GBM proliferation and reduced animal survival, whereas suppression of Hfe induced apoptotic cell death and extended survival, which was more pronounced in females and associated with attenuation of natural killer cells and CD8+ T cell activity. Analysis of iron gene signatures in Hfe-knockdown cells revealed alterations in the expression of several iron-associated genes, suggesting global disruption of intracellular iron homeostasis. Further analysis of differentially expressed pathways revealed oxidative stress as the top pathway upregulated following Hfe loss. Hfe knockdown indeed resulted in enhanced 55Fe uptake and generation of reactive oxygen species. Conclusions These findings reveal an essential function for HFE in GBM cell growth and survival, as well as a sex-specific interaction with the immune response.
Soil carbon and nitrogen dynamics along replicated chronosequences of abandoned agricultural lands in southeastern Ontario

ii ACKNOWLEDGEMENTS . iv CO-AUTHORSHIP. v TABLE OF CONTENTS. vi LIST OF TABLES. vii LIST OF FIGURES. ix CHAPTER 1: GENERAL INTRODUCTION. 1 CHAPTER 2: A CENTURY OF SOIL CARBON DYNAMICS ALONG CHRONOSEQUENCES OF ABANDONED AGRICULTURAL LANDS IN A CANADIAN TEMPERATE FOREST ECOZONE. 6 ABSTRACT. 6 INTRODUCTION. 7 METHODS. 12 RESULTS.20 DISCUSSION. 25 TABLES AND FIGURES. 35 CHAPTER 3: SOIL NET NITROGEN CYCLING RATES AND MICROBIAL BIOMASS DURING TEMPERATE FOREST SUCCESSION USING A 100-YEAR REPLICATED CHRONOSEQUENCE. 46 ABSTRACT. 46 INTRODUCTION.47 METHODS. 50 RESULTS. 56 DISCUSSION. 59 TABLES AND FIGURES. 66 CHAPTER 4: GENERAL DISCUSSION. 75 LITERATURE CITED. 78 APPENDIX 1: INORGANIC CARBON. 89 APPENDIX 2: CONSTANT MASS. 90 APPENDIX 3: CONSTANT MASS. 91 APPENDIX 4: SITE DESCRIPTIONS . 92 APPENDIX 5: SITE DESCRIPTIONS . 92
External Periodic Field and Skew-chG Beams for Generation of Multifocal Terahertz Radiation in Collisional Plasma

A riveting mechanism for the generation of efficient THz radiation is shown by creating an extra transverse component of the nonlinear ponderomotive force (i.e., the induced force) on the application of an external periodic electric field to the collisional plasma having density ripples. Different components of nonlinear currents are produced due to the generation of different components of the force, i.e., one beating component and two coupling components. These nonlinear currents result in the emission of THz fields, namely a beat wave THz field, <inline-formula> <tex-math notation="LaTeX">$({E}_{\mathrm{ 0THz1}})_{\mathrm{ Beating}}$ </tex-math></inline-formula> and induced THz fields, <inline-formula> <tex-math notation="LaTeX">$(\text {IE}_{\mathrm{ THz2}})_{\text {EF}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$(\text {IE}_{\mathrm{ THz3}})_{\text {EF}}$ </tex-math></inline-formula>. The multiple resonance conditions and tuning of the amplitude of emitted THz radiations are achieved by the periodicities and amplitudes of density ripples (<inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>) and external electric field (<inline-formula> <tex-math notation="LaTeX">$\xi $ </tex-math></inline-formula>). A comparative study between the beat wave-enabled field and induced fields signifies that the former is <inline-formula> <tex-math notation="LaTeX">${10}^{2}$ </tex-math></inline-formula> times higher in magnitude than the latter. The collisions between electrons and neutrals mainly affect the amplitude of the emitted THz radiations when the frequency of collisions (<inline-formula> <tex-math notation="LaTeX">$\upsilon $ </tex-math></inline-formula>) is greater or equal to <inline-formula> <tex-math notation="LaTeX">$0.25\omega _{p}$ </tex-math></inline-formula>; <inline-formula> <tex-math notation="LaTeX">$\omega _{p}$ </tex-math></inline-formula> being the plasma frequency. The skew- chG beams are employed in the plasma medium due to the sharp gradient in their laser profile and the dip in their peaks that can be easily controlled by the skewness parameter. It is worth noting that the amplitude of any THz field can be tuned by changing the parameters regarding lasers, external electric field, and plasma. The emitted fields are convertible, multifocal, intensity tunable, and are highly efficient with the efficiency of <inline-formula> <tex-math notation="LaTeX">$2.25\times 10^{-4}$ </tex-math></inline-formula>.
Dynamic Average Consensus of Time-Varying Signals With Known Frequency Components

The dynamic average consensus problem, a group of agents, each associated with a time-varying signal, reaching consensus at the average of these signals by their own distributed estimators that interact with each other through the communication network, finds many applications such as distributed estimation, formation control and sensor fusion. Many distributed estimators have been constructed that achieve either consensus precisely at the average of the signals or around it depending on the properties of the signals. In this paper, we revisit the dynamic average consensus problem in both the continuous-time and discrete-time settings. By utilizing the information on the frequency components of the signals, we construct distributed estimators that achieve accurate consensus at the average of the signals. We further establish that our distributed estimators are robust to the interruption of the network connectivity in the sense that connected subgroups of agents will continue to reach consensus around the average of all signals after an interruption occurs as along as the signals are bounded and the later the interruption occurs the more accurate the consensus will be. Numerical simulation is carried out to illustrate the theoretical results.
Sedimentary structures and palaeocurrents of the Siwaliks exposed between the Yamuna and Gola Rivers, U.P. (India)

Summary The present paper deals with sedimentary structures and palaeocurrents of the Siwaliks exposed between the Yamuna and Gola rivers, U.P., India. Typical sedimentary structures observed in the Lower and Middle Siwalik formations are: cross-bedding, rib-and-furrow structure, parting lineation, plane-laminations and worm burrows. Also the Lower and Middle Siwaliks exhibit a typical, cyclic development of sedimentary bed forms and structures characteristic of flood plain deposits. A study of cross-bedding foreset azimuths indicates south-westerly and southerly paleocurrent directions for the Lower and Middle Siwaliks respectively. Distributions of pebble orientations from the Upper Siwalik have significant preferred orientations along both N–S and E–W directions.