Miracle-dz/UniFAR-FTUs huggingface dataset:
FCNet: Learning Noise-Free Features for Point Cloud Denoising

The acquisition of point clouds is usually accompanied by noise due to imperfect laser scanning or image-based reconstruction techniques. Deep learning-based methods have achieved impressive performance in point cloud denoising. However, the features captured by a denoising network from noisy point clouds are usually contaminated by noise during training. The feature noise will lead to the oscillation of back-propagated gradients, which interferes with parameter optimization and reduces the denoising performance. In this paper, we propose to explicitly clean up feature noise for point cloud denoising from two aspects: feature noise cleaning and network training. From the first aspect, we propose the feature clean network (FCNet for short) to explicitly clean up the feature noise. From the second aspect, we train FCNet by a teacher-student learning model to learn the noise-free features under the guidance of feature domain losses. Specifically, FCNet is designed with emphasis on two modules: non-local self-similarity (NSS) and weighted average pooling (WAP). NSS module smooths features through a non-local filter based on the inherent non-local self-similarity of point clouds. WAP module applies original weights calculated by the statistical outlier removal algorithm to suppress the feature noise induced by outliers. In the teacher-student learning model, we introduce a clean input using the noisy point and its clean neighbors. The teacher network accepts the clean input to capture noise-free features. The student network is trained to imitate the teacher network to learn noise-free features by minimizing the feature loss. The experiments on synthetic and real scanned point clouds show that FCNet outperforms state-of-the-art point cloud denoising methods.
Histopathology and indicators of borderline ovarian tumours with microinvasion in bitches

The authors present two cases of borderline ovarian tumours with microinvasion in bitches with variable clinical significance. The first case documents a four-year-old female Weimaraner diagnosed with a tumour on the right ovary during a veterinary check-up, using ultrasound (USG) examination, which was then surgically removed. Histological examination revealed a clear cell borderline tumour of the ovary with microinvasion. The second case is represented by a necropsy sample from a 52-month-old female German Shepherd who died a day before the planned hysterectomy due to undertreated pyometra. During necropsy, a developed bilateral ovarian tumour was found. An additional histological examination revealed a serous borderline tumour with microinvasion of both ovaries. This paper discusses the histopathological and clinical aspects involved in the prognosis of borderline ovarian tumours in bitches. This concerns the possibility of a change for a more aggressive behaviour of these tumours and their immunohistochemical profile, then the risk of implant metastases and, finally, the time point of diagnosis, intervention, and therapy. Even histologically verified well-differentiated forms of borderline ovarian tumours with microinvasion in bitches can show the variable clinical significance and, therefore, in similar cases, only a good or only a bad prognosis of the disease should not be expected.
Optimal L2 velocity error estimate for a modified pressure-robust Crouzeix–Raviart Stokes element

Recently, a novel approach for the robust discretization of the incompressible Stokes equations was proposed that slightly modifies the nonconforming Crouzeix–Raviart element such that its velocity error becomes pressure-independent. The modification results in an O(h) consistency error that allows straightforward proofs for the optimal convergence of the discrete energy norm of the velocity and of the L norm of the pressure. However, though the optimal convergence of the velocity in the L norm was observed numerically, it appeared to be nontrivial to prove. In this contribution, this gap is closed. Moreover, the dependence of the energy error estimates on the discrete inf-sup constant is traced in detail, which shows that classical error estimates are extremely pessimistic on domains with large aspect ratios. Numerical experiments in 2D and 3D illustrate the theoretical findings.
Autoreactivity against Denatured Type III Collagen Is Significantly Decreased in Serum from Patients with Cancer Compared to Healthy Controls

Autoantibodies have the potential as cancer biomarkers as they may associate with the outcome and immune-related adverse events (irAEs) following immunotherapy. Cancer and other fibroinflammatory diseases, such as rheumatoid arthritis (RA), are associated with excessive collagen turnover leading to collagen triple helix unfolding and denaturation with exposure of immunodominant epitopes. In this study, we aimed to investigate the role of autoreactivity against denatured collagen in cancer. A technically robust assay to quantify autoantibodies against denatured type III collagen products (anti-dCol3) was developed and then measured in pretreatment serum from 223 cancer patients and 33 age-matched controls. Moreover, the association between anti-dCol3 levels and type III collagen degradation (C3M) and formation (PRO-C3) was investigated. Anti-dCol3 levels were significantly lower in patients with bladder (p = 0.0007), breast (p = 0.0002), colorectal (p < 0.0001), head and neck (p = 0.0005), kidney (p = 0.005), liver (p = 0.030), lung (p = 0.0004), melanoma (p < 0.0001), ovarian (p < 0.0001), pancreatic (p < 0.0001), prostate (p < 0.0001), and stomach cancers (p < 0.0001) compared to controls. High anti-dCol3 levels were associated with type III collagen degradation (C3M, p = 0.0002) but not type III collagen formation (PRO-C3, p = 0.26). Cancer patients with different solid tumor types have downregulated levels of circulating autoantibodies against denatured type III collagen compared to controls, suggesting that autoreactivity against unhealthy type III collagen may be important for tumor control and eradication. This autoimmunity biomarker may have the potential for studying the close relationship between autoimmunity and cancer.
Anionic Polysaccharide Cryogels: Interaction and In Vitro Behavior of Alginate–Gum Arabic Composites

In the present study, polysaccharide-based cryogels demonstrate their potential to mimic a synthetic extracellular matrix. Alginate-based cryogel composites with different gum arabic ratios were synthesized by an external ionic cross-linking protocol, and the interaction between the anionic polysaccharides was investigated. The structural features provided by FT-IR, Raman, and MAS NMR spectra analysis indicated that a chelation mechanism is the main process linking the two biopolymers. In addition, SEM investigations revealed a porous, interconnected, and well-defined structure suitable as a scaffold in tissue engineering. The in vitro tests confirmed the bioactive character of the cryogels through the development of the apatite layer on the surface of the samples after immersion in simulated body fluid, identifying the formation of a stable phase of calcium phosphate and a small amount of calcium oxalate. Cytotoxicity tests performed on fibroblast cells demonstrated the non-toxic effect of alginate–gum arabic cryogel composites. In addition, an increase in flexibility was noted for samples with a high gum arabic content, which determines an appropriate environment to promote tissue regeneration. The newly obtained biomaterials that exhibit all these properties can be successfully involved in the regeneration of soft tissues, wound management, or controlled drug release systems.
Tunable MEMS Fabry-Pérot filters for infrared microspectrometers: a review

Many application fields of infrared spectroscopy require small, robust and transportable spectrometers, which are considerable less costly than existing products. Therefore microspectrometer technologies are rapidly emerging and many research groups spend effort on this. Compared to other kinds of devices, micromachined tunable Fabry-Pérot filters are best suited in terms of miniaturization and optical throughput. This paper gives a review of μFP filters for infrared spectroscopy. Different approaches from several groups are compared. Optical performance parameters like wavelength tuning range, spectral resolution and aperture size as well as complexity of fabrication and costs are discussed.
Prevalence of depression in patients on hemodialysis

Objective: To determine the prevalence of depression in patients undergoing hemodialysis. Study Design: Cross-sectional study. Place and Duration: Institute of Kidney Diseases Hayatabad, Peshawar and PAF Hospital Islamabad for the duration from December 2021 to May 2022. Methodology: Two hundred and twenty patients on hemodialysis of either gender with ages 25-70 years were enrolled. Detailed demographics were recorded after taking informed written consent. Patients' Health Questionnaire-9 (PHQ-9) questions were used to determine the frequency of depression. Severity of depression was also examined. Data was analyzed by SPSS 24.0. Results: There were 135 (61.36%) males while 85 (38.64%) patients were females. Mean age of patients was 46.85±12.38 years. Mean time duration of hemodialysis was 5.25±2.46 years. Depression was found in 160 (72.73%) patients, among them 30 (13.64%) patients had mild, 75 (34.09%) had moderate and 55 (25%) had severe depression. Patients on hemodialysis less than 5 years found more depressive as compared to >5 years. (p-value <0.05). Conclusion: It is concluded that the prevalence of depression in patients of hemodialysis was high.
Recognition of Hand Gestures Based on EMG Signals with Deep and Double-Deep Q-Networks

In recent years, hand gesture recognition (HGR) technologies that use electromyography (EMG) signals have been of considerable interest in developing human–machine interfaces. Most state-of-the-art HGR approaches are based mainly on supervised machine learning (ML). However, the use of reinforcement learning (RL) techniques to classify EMGs is still a new and open research topic. Methods based on RL have some advantages such as promising classification performance and online learning from the user’s experience. In this work, we propose a user-specific HGR system based on an RL-based agent that learns to characterize EMG signals from five different hand gestures using Deep Q-network (DQN) and Double-Deep Q-Network (Double-DQN) algorithms. Both methods use a feed-forward artificial neural network (ANN) for the representation of the agent policy. We also performed additional tests by adding a long–short-term memory (LSTM) layer to the ANN to analyze and compare its performance. We performed experiments using training, validation, and test sets from our public dataset, EMG-EPN-612. The final accuracy results demonstrate that the best model was DQN without LSTM, obtaining classification and recognition accuracies of up to 90.37%±10.7% and 82.52%±10.9%, respectively. The results obtained in this work demonstrate that RL methods such as DQN and Double-DQN can obtain promising results for classification and recognition problems based on EMG signals.
Adherence to Mediterranean Diet: A Population-Based Longitudinal Cohort Study

Adherence to the Mediterranean diet (MedDiet) is recommended for cardiovascular disease prevention. However, recent epidemiological studies report a shift toward lower adherence to MedDiet. We have conducted a prospective cohort study to evaluate changes in individual determinants of MedDiet adherence over time. Clinical information and MedDiet adherence score (MEDAS) were collected in 711 subjects (mean age 68 ± 10 years; 42% males), enrolled in the PLIC study (Progression of Intimal Atherosclerotic Lesions in Carotid arteries), during two visits conducted, on average, 4.5 years apart. MEDAS score worsening and improvements (absolute change, ΔMEDAS) and the variation in the proportion of subjects reporting to meet each MEDAS criteria were assessed. Overall, 34% of the subjects improved their MedDiet adherence (ΔMEDAS: +1.87 ± 1.13), by consuming more olive oil, legumes and fish and use of dishes seasoned with sofrito and 48% subjects worsened their MedDiet adherence (ΔMEDAS: −2.02 ± 1.14) by consuming less fruit, legumes, fish and nuts, with higher rates of worsening in women and subjects aged 50–65 years. Subjects who improved the score were more obese, had higher plasma glucose levels, and metabolic syndrome at the basal visit. In summary, we report an overall decrease in MedDiet adherence, evaluated during a timeframe heavily affected by the COVID-19 pandemic, underlining the need for better dietary interventions.
Spectral Characteristics and Functional Responses of Phospholipid Bilayers in the Terahertz Band

Understanding the vibrational information encoded within the terahertz (THz) spectrum of biomolecules is critical for guiding the exploration of its functional responses to specific THz radiation wavelengths. This study investigated several important phospholipid components of biological membranes—distearoyl phosphatidylethanolamine (DSPE), dipalmitoyl phosphatidylcholine (DPPC), sphingosine phosphorylcholine (SPH), and lecithin bilayer—using THz time-domain spectroscopy. We observed similar spectral patterns for DPPC, SPH, and the lecithin bilayer, all of which contain the choline group as the hydrophilic head. Notably, the spectrum of DSPE, which has an ethanolamine head group, was different. Interestingly, density functional theory calculations confirmed that the absorption peak common to DSPE and DPPC at approximately 3.0 THz originated from a collective vibration of their similar hydrophobic tails. Accordingly, the cell membrane fluidity of RAW264.7 macrophages with irradiation at 3.1 THz was significantly enhanced, leading to improved phagocytosis. Our results highlight the importance of the spectral characteristics of the phospholipid bilayers when studying their functional responses in the THz band and suggest that irradiation at 3.1 THz is a potential non-invasive strategy to increase the fluidity of phospholipid bilayers for biomedical applications such as immune activation or drug administration.
Better imaging for landmine detection : an exploration of 3D full-wave inversion for ground-penetrating radar

Humanitarian clearance of minefields is most often carried out by hand, conventionally using a a metal detector and a probe. Detection is a very slow process, as every piece of detected metal must treated as if it were a landmine and carefully probed and excavated, while many of them are not. The process can be safely sped up by use of Ground-Penetrating Radar (GPR) to image the subsurface, to verify metal detection results and safely ignore any objects which could not possibly be a landmine. In this thesis, we explore the possibility of using Full Wave Inversion (FWI) to improve GPR imaging for landmine detection. Posing the imaging task as FWI means solving the large-scale, non-linear and ill-posed optimisation problem of determining the physical parameters of the subsurface (such as electrical permittivity) which would best reproduce the data. This thesis begins by giving an overview of all the mathematical and implementational aspects of FWI, so as to provide an informative text for both mathematicians (perhaps already familiar with other inverse problems) wanting to contribute to the mine detection problem, as well as a wider engineering audience (perhaps already working on GPR or mine detection) interested in the mathematical study of inverse problems and FWI.We present the first numerical 3D FWI results for GPR, and consider only surface measurements from small-scale arrays as these are suitable for our application. The FWI problem requires an accurate forward model to simulate GPR data, for which we use a hybrid finite-element boundary-integral solver utilising first order curl-conforming N\'{e}d\'{e}lec (edge) elements. We present a novel `line search' type algorithm which prioritises inversion of some target parameters in a region of interest (ROI), with the update outside of the area defined implicitly as a function of the target parameters. This is particularly applicable to the mine detection problem, in which we wish to know more about some detected metallic objects, but are not interested in the surrounding medium. We may need to resolve the surrounding area though, in order to account for the target being obscured and multiple scattering in a highly cluttered subsurface.We focus particularly on spatial sensitivity of the inverse problem, using both a singular value decomposition to analyse the Jacobian matrix, as well as an asymptotic expansion involving polarization tensors describing the perturbation of electric field due to small objects. The latter allows us to extend the current theory of sensitivity in for acoustic FWI, based on the Born approximation, to better understand how polarization plays a role in the 3D electromagnetic inverse problem. Based on this asymptotic approximation, we derive a novel approximation to the diagonals of the Hessian matrix which can be used to pre-condition the GPR FWI problem.
Sustainability reporting, gender diversity, firm value and corporate performance in ASEAN region

Abstract This paper aimed to examine the influences of sustainability reporting and gender diversity on corporate outcomes of listed companies in ASEAN region. The listed companies in ASEAN region consisting of Thailand, Malaysia, Indonesia, and the Philippines during 2010 to 2019 were used as population and sample of this study. Seven key performance indexes of the Global Reporting Initiative (GRI) Standards, Carbon Disclosure Project (CDP), Carbon Knights, and Refinitiv were used to measure the extent and level of sustainability reporting, while the proportion of female board committee was used to measure gender diversity, and firm value measured by Tobin’s Q and firm performance measured by return on asset (ROA) were used as the proxies of corporate outcomes. The sequential logit regression models were developed to test the hypotheses used in this study. This study found positive impact of energy used, water management, work safety, and gender diversity on corporate performance, while corporate performance was influenced by carbon emission and waste management negatively. Moreover, gender diversity moderated the negative relationship between energy used, water management, work safety, and corporate performance. On the other hand, there was water management, and work safety influencing firm value positively, while waste management had negative impact on firm value. However, gender diversity did not moderate any relationship between sustainability reporting and firm value. The study findings benefit to top management who may encourage in sustainability reporting especially water management and work safety to enhance the corporate performance and value.
Organisational justice and teachers’ job commitment in Kwara State Public Primary Schools, Nigeria

Abstract This study sought to investigate the relationship between organisation justice and teachers’ job commitment in Kwara State public primary schools, Nigeria. The study adopted a descriptive survey of correlation type. Random sampling technique was used to select 72 schools out of 1,580 public primary schools in the three senatorial districts of the state. Fifteen respondents from each sampled school making a total of 1,080 respondents for the study. The instruments used were: “Organisational Justice and “Teachers’ Job Commitment Questionnaire”. The instruments were validated by experts in the Department of Educational Management. One thousand and eighty (1,080) copies of questionnaire were administered to teachers but nine hundred and seventy two (972) copies returned. Two research questions were raised and answered; one hypothesis was formulated to guide the study. Descriptive Statistics of mean and standard deviation were used to answer the research questions while step-wise Multiple Regression was used to test the hypotheses at 0.05 level of significance. The findings were that: the level of organizational justice was found to be average; the level of teachers’ job commitment was also found to be average; distributive justice and procedural justice have negative significant relationship with teachers’ job commitment while informational justice and interactional justice have positive and significantly correlate with teachers’ job commitment. The study concluded that both the informational justice and interactional justice will enhance and improve organizational justice as well as teachers’ commitment in schools. The study recommended, among other, that the head teachers should ensure consistency in applying rules, maintain accuracy in the use information, and safeguard against bias when dealing with teachers’ issues, Also, the head teachers should involve staff members in decision making that affect them and show dignity and respect when explaining decisions outcomes to concerned teachers.
Signal-Retaining Autophagy Indicator as a Quantitative Imaging Method for ER-Phagy

Autophagy is an intracellular lysosomal degradation pathway by which cytoplasmic cargoes are removed to maintain cellular homeostasis. Monitoring autophagy flux is crucial to understand the autophagy process and its biological significance. However, assays to measure autophagy flux are either complex, low throughput or not sensitive enough for reliable quantitative results. Recently, ER-phagy has emerged as a physiologically relevant pathway to maintain ER homeostasis but the process is poorly understood, highlighting the need for tools to monitor ER-phagy flux. In this study, we validate the use of the signal-retaining autophagy indicator (SRAI), a fixable fluorescent probe recently generated and described to detect mitophagy, as a versatile, sensitive and convenient probe for monitoring ER-phagy. This includes the study of either general selective degradation of the endoplasmic reticulum (ER-phagy) or individual forms of ER-phagy involving specific cargo receptors (e.g., FAM134B, FAM134C, TEX264 and CCPG1). Crucially, we present a detailed protocol for the quantification of autophagic flux using automated microscopy and high throughput analysis. Overall, this probe provides a reliable and convenient tool for the measurement of ER-phagy.