Miracle-dz/UniFAR-FTUs huggingface dataset:
SyMBac: Synthetic Micrographs for Training Deep Neural Networks for Accurate Segmentation of Bacterial Cells

We present a novel method of bacterial image segmentation using machine learning based on Synthetic Micrographs of Bacteria (SyMBac). SyMBac allows for rapid, automatic creation of arbitrary amounts of training data that combines detailed models of cell growth, physical interactions, and microscope optics to create synthetic images which closely resemble real micrographs, with access to the ground truth positions of cells. This approach provides the following major advantages: (1) Synthetic training data can be generated virtually instantly, and on demand; (2) it is accompanied by perfect ground truth information, which also means large sets of training data can be generated without the need for curation; (3) Biological ground truth and imaging effects can be tuned independently, enabling easy adaptation to different biological conditions, imaging platforms, and imaging modalities. Machine-learning models trained on SyMBac data generate more accurate and precise cell masks than those trained on human annotated data. This enables precise analysis of cell size regulation along changing conditions in a growing bacterial cell culture, revealing novel insights about the physiological dynamics of individual bacterial cells during entry and exit from dormancy.
England's policy on severe mental illness

RIASSUNTO La Gran Bretagna ha messo a punto una comprovata strategia sulla salute mentale che include dove e come le persone sono curate e con quali obiettivi. Ha assicurato che la politica sanitaria sia basata sull'epidemiologia dei disturbi psichici e che sia incentrata non solo sui disturbi psichici gravi, che necessitano di trattamenti specifici, ma anche sui disturbi meno gravi in medicina di base, nelle carceri e sul posto di lavoro ed inoltre sull'integrazione e interfaccia tra i servizi di salute mentale ed altre agenzie. Ha basato la politica sanitaria su un quadro coerente di promozione della prevenzione della salute mentale (prevenzione primaria, secondaria e terziaria e prevenzione della mortalita) conduce questa politica quantificando l'esito in termini di salute mentale, nonche gli imputs ed i processi. SUMMARY England has set an evidence based mental health strategy which encompasses where and how people are cared for and with what aims and goals in view. It has ensured that policy is well rooted in the known epidemiology of mental disorders, that policy does not only focus on those with severe mental disorders who need specific care, but also on those with less severe disorders in primary care, in the workplace and in prisons, and also on the integration and interface of mental health services with other agencies. It has also rooted policy within a coherent framework of prevention mental health promotion, primary, secondary and tertiary prevention and prevention of mortality), and is driving that policy by measuring health outcomes as well as inputs and processes.
Research on Phased Array Radar Resource Management in Searching Mode

Multifunction phased array radar, with rational management of time resource and energy resource, can search and track multiple targets simultaneously. A resource management optimization model for phased array radar in searching mode has been proposed, whose objection is to minimize the total resource consumption under the guarantee of searching performance with controllable parameters: search frame period, dwell time and signal duty circle. Both radial cumulative detection probability and tangential cumulative detection probability are considered in the searching performance. The consideration of tangential cumulative detection probability as one of the constraints is the main contribution here. The solution to this model by applying genetic algorithm suggests that this model is more suitable in actual scene.
East Meets West in Therapeutic Approaches to the Management of Chronic Pain

Yoga as a holistic principle, not only practice of asanas or poses, integrates all aspects of the self, with biological, mental, intellectual, and spiritual elements. Yoga encompasses the biopsychosocial medical perspective, which regards pain as a dynamic interaction between physiological, psychological, and social factors. The purpose of this perspective article is to compare and contrast psychological practices such as mindfulness meditation, relaxation response (RR), and cognitive behavioral therapy (CBT) with Yoga in their management of chronic pain. The use of these practices is explored through history, literature, and research studies. Results from scientific studies on Yoga show changes in health-related pain outcomes for patients with chronic pain. The key aspects of Yoga, notably relaxation, positive thinking, and mindfulness, are discussed in relation to mindfulness meditation, RR, and CBT.
Development and validation of an offline deep learning algorithm to detect vitreoretinal abnormalities on ocular ultrasound

Purpose: We describe our offline deep learning algorithm (DLA) and validation of its diagnostic ability to identify vitreoretinal abnormalities (VRA) on ocular ultrasound (OUS). Methods: Enrolled participants underwent OUS. All images were classified as normal or abnormal by two masked vitreoretinal specialists (AS, AM). A data set of 4902 OUS images was collected, and 4740 images of satisfactory quality were used. Of this, 4319 were processed for further training and development of DLA, and 421 images were graded by vitreoretinal specialists (AS and AM) to obtain ground truth. The main outcome measures were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under receiver operating characteristic (AUROC). Results: Our algorithm demonstrated high sensitivity and specificity in identifying VRA on OUS ([90.8%; 95% confidence interval (CI): 86.1–94.3%] and [97.1% (95% CI: 93.7–98.9%], respectively). PPV and NPV of the algorithm were also high ([97.0%; 95% CI: 93.7–98.9%] and [90.8%; 95% CI: 86.2–94.3%], respectively). The AUROC was high at 0.939, and the intergrader agreement was nearly perfect with Cohen's kappa of 0.938. The model demonstrated high sensitivity in predicting vitreous hemorrhage (100%), retinal detachment (97.4%), and choroidal detachment (100%). Conclusion: Our offline DLA software demonstrated reliable performance (high sensitivity, specificity, AUROC, PPV, NPV, and intergrader agreement) for predicting VRA on OUS. This might serve as an important tool for the ophthalmic technicians who are involved in community eye screening at rural settings where trained ophthalmologists are not available.
Diagnostic accuracy and clinical utility of bed side tests versus laboratory tests in the diagnosis of ocular myasthenia

Purpose: To assess the diagnostic accuracy of forced eyelid closure test (FECT), ice pack test (IPT), repetitive nerve stimulation test (RNS), and acetylcholine receptor (AchR) antibody test in patients with suspected ocular myasthenia. To assess the clinical utility of AchR antibody test in predicting disease progression. Methods: Retrospective cohort study of patients diagnosed with ocular myasthenia at a South-Indian neuro-ophthalmology tertiary eye clinic. Baseline characteristics; ocular myasthenia symptoms; results of FECT, IPT, RNS, and AchR antibody test; and progression time to generalized myasthenia (GM) over 36 months from the time of diagnosis were recorded and analyzed using receiver operator curve analysis, multiple logistic regression, and Kaplan–Meier survival analysis. Results: FECT had a sensitivity of 96.7% (95% CI: 88.5–99.6) and a specificity of 75% (95% CI: 34.9–96.8). Combination of FECT and IPT, using the positivity of at least one test, increased the sensitivity to 98.3% (95% CI: 91–100), reducing the specificity to 50% (95% CI: 15.7–84.3), whereas using the positivity of both tests, we obtained a sensitivity of 71.7% (95% CI: 58.6–82.5) and a specificity of 100% (95% CI: 63.1–100). In the subset of patients with double negative RNS and AchR antibodies, the positive predictive value of combined FECT and IPT (double positive) was 100%. Patients who developed GM were more likely to have a positive AchR antibody test result (P = 0.001). Conclusion: Combined FECT and IPT (double positive) has high diagnostic accuracy even among patients with normal RNS and negative AchR antibodies. Despite low sensitivity, AchR-antibody test has a significant predictive value in disease progression.
KeypointMorph: Robust Multi-modal A � ne Registration via Unsupervised Keypoint Detection

Registration is a fundamental task in medical imaging, and recent machine learning meth-ods have become the state-of-the-art. However, these approaches are often not inter-pretable, lack robustness to large misalignments, and do not incorporate symmetries of the problem. In this work, we propose KeypointMorph, an unsupervised end-to-end learning-based image registration framework that relies on automatically detecting corresponding keypoints. Our core insight is straightforward: matching keypoints between images can be used to obtain the optimal transformation via a di ↵ erentiable closed-form expression. We use this observation to drive the unsupervised learning of anatomically-consistent keypoints from images. This not only leads to substantially more robust registration but also yields better interpretability, since the keypoints reveal which parts of the image are driving the ﬁnal alignment. Moreover, KeypointMorph can be designed to be equivariant under image translations and/or symmetric with respect to the input image ordering. We demonstrate the proposed framework in solving 3D a � ne registration of multi-modal brain MRI scans. Remarkably, we show that this strategy leads to consistent keypoints, even across modalities. We demonstrate registration accuracy that surpasses current state-of-the-art methods, especially in the context of large displacements. Our code is available at URL 1
Learning Structured Sparse Matrices for Signal Recovery via Unrolled Optimization

Countless signal processing applications include the reconstruction of an unknown signal from very few indirect linear measurements. Because the measurement operator is commonly constrained by the hardware or the physics of the observation process, ﬁnding measurement matrices that enable accurate signal recovery poses a challenging discrete optimization task. Meanwhile, recent advances in the ﬁeld of machine learning have highlighted the effectiveness of gradient-based optimization methods applied to large computational graphs such as those arising naturally when unrolling iterative algorithms for signal recovery. However, it has remained unclear how to leverage this technique when the set of admissible measurement matrices is both discrete and sparse. In this paper, we tackle this problem and propose an efﬁcient and ﬂexible method for learning structured sparse measurement matrices. Our approach uses unrolled optimization in conjunction with Gumbel reparametrizations. We empirically demonstrate the effectiveness of our method in two prototypical compressed sensing situations.
Fault Diagnosis Method of Rolling Bearings Based on Ensemble Empirical Mode Decomposition Energy Entropy and LS-SVM

In view of the non-stationary features of vibration signals of rolling bearings and the difficulty to collect a large number of fault samples in practice, a fault diagnosis scheme based on ensemble empirical mode decomposition(EEMD) energy entropy and least square support vector machine(LS-SVM) is proposed. First of all, original acceleration vibration signals are decomposed into several intrinsic mode functions(IMFs). Since the energy of vibration signal changes in different frequency bands when fault occurs, the fault pattern and condition can be identified by calculating and analyzing the EEMD energy entropy. The energy features extracted from several IMFs, which contain the most dominant fault information, serve as the input vector of the support vector machine to judge the technical condition of the bearing and seriousness of the fault. The diagnosis results are analyzed and compared with different kernel functions. The experimental results show that the proposed method is effective.
Predicting Fine-Tuning Performance with Probing

Large NLP models have recently shown impressive performance in language understanding tasks, typically evaluated by their fine-tuned performance. Alternatively, probing has received increasing attention as being a lightweight method for interpreting the intrinsic mechanisms of large NLP models. In probing, post-hoc classifiers are trained on “out-of-domain” datasets that diagnose specific abilities. While probing the language models has led to insightful findings, they appear disjointed from the development of models. This paper explores the utility of probing deep NLP models to extract a proxy signal widely used in model development – the fine-tuning performance. We find that it is possible to use the accuracies of only three probing tests to predict the fine-tuning performance with errors 40% - 80% smaller than baselines. We further discuss possible avenues where probing can empower the development of deep NLP models.
Being Nice or Being Scared? Personality Traits, Beliefs and Threat of COVID-19 as Predictors of Non-Normative Health Behaviors during Second Wave of Pandemic

The COVID-19 pandemic highlighted the need to examine factors related to questionable health behavior, such as avoiding recommended preventive guidelines. This paper aimed to explore whether behavior reflecting active avoidance of preventive measures against COVID-19 (curfew regulations, hygiene, facial masks, and social distancing) was best predicted by personality traits (Big Five), health beliefs, or feelings of threat. Thousand and twenty-four adults (486 men, 536 women) aged between 18 - 81 years participated in the study, which was run in early November 2020, when the second wave in Slovakia started to gain momentum and a strict lockdown was issued. Results showed that health threat was connected with having fewer questionable health beliefs, while economic threat was connected with having more questionable health beliefs, and together these factors were the strongest predictors of avoiding preventive regulations. From personality traits, higher Extraversion and lower Agreeableness predicted questionable health behavior, but together they added only 2.4% of explained variance. Our results highlight the fact that one year after the outbreak, the COVID-19 pandemic is no longer only (if it ever was) a health threat. The shift from health focus to the economic and socio-political threat should not be taken lightly, as it has implications for adherence to preventive measures against COVID-19 and people's beliefs regarding the pandemic.
Re-analysing the Data From Moffatt et al. (2020): What Can We Learn From an Under-powered Absence of Difference?

Moffatt et al. (2020) reported the results of an experiment (N = 26 in the final sample) comparing the facial electromyographic correlates of mental rumination and distraction, following an experimentally induced stressor. Based on the absence of significant difference (and BFs between 3.6 and 4.3) in the perioral muscular activity between the rumination and distraction conditions, Moffatt et al. (2020) concluded that self-reported inner experience was unrelated to peripheral muscular activity as assessed using surface electromyography. In this short commentary, we show that there is limited evidence for the main conclusion put forward by Moffatt et al. (2020), and we suggest ways forward, both from a theoretical and a methodological perspective. Complete source code, reproducible analyses, and figures are available at https://osf.io/ba3gk/.
Decentralized Identity Management for E-Health Applications: State-of-the-Art and Guidance for Future Work

Background The increasing use of various online services requires an efficient digital identity management (DIM) approach. Unfortunately, the original Internet protocols were not designed with built-in identity management, which creates challenges related to privacy, security, and usability. There is an increasing societal concern regarding the management of these sensitive data, access to it, and where it is stored. Blockchain technology can potentially offer a secure solution to address these issues in a decentralized manner without centralized authority. This is important for e-health services where the patient and the healthcare provider often are required to prove their identity. Blockchain technology can be utilized for creating digital identities and making its management easier, thus giving a higher degree of control to the user than what current solutions offer. It can be used to create a digital identity on the blockchain, making it easier for individuals and entities to manage, giving them greater control over who has their personal information and how they handle it. In addition, it might be utilized to create a higher degree of trust and security for e-health applications. Objective The aim of this research work was to review the state-of-the-art regarding blockchain-based decentralized identity management for healthcare applications. Based on this summary, we provide a viewpoint on how blockchain-based decentralized identity frameworks might be utilized for virtualized healthcare applications. Methods This research study applied a scoping, semi-systematic review approach to summarize the state-of-the-art. Included identity management systems were evaluated based on seven criteria: autonomy, authority, availability, approval, confidentiality, tenacity, and Interoperability. Results Seven blockchain-based identity management systems were included and evaluated in this work: these include solutions built with Ethereum, Hyperledger Indy, Hyperledger Fabric, Hedera, and Sovrin blockchains. Conclusions DIM is crucial for virtual health care. Decentralized identity management for healthcare purposes is currently being explored in both academia and the private sector. More work is needed with the aim of improving the efficiency of current DIM solutions and to fully understand what technical frameworks are best suited for e-health applications.
Ensuring Effective Secure Learning Environment using Authentication System in Terms of Cost and Time at Kumasi Technical University, Ghana

A welcoming environment is important to promote learning activities in a tertiary institution. Students’ violence has become a major concern in developing countries like Ghana. There is therefore the need to provide a system that will offer assistance to ensure or minimize this violence from students in other to provide tranquil learning environment. With the use of technology like Radio-frequency identification (RFID) the security of Kumasi Technical University (KsTU) can be up graded. Considering the enrollment of staff and the number of students, physical checks for security will demand time and cost. In this paper, a system which does not consume time, very efficient and cost effective will be utilized to oversee the physical security of KsTU. RFID and Local temporary capacity administration can also be used to manage student’s attendance database server. Two algorithms will be presented for efficient communication between the database server and nearby gadgets which will be raspberry pi. A discussion will be done to assess the time and cost involved in student verification authentication process and attendance. This paper is a theoretical analysis of the proposed system.
Soluble Programmed Cell Death Protein 1 and Its Ligand: Potential Biomarkers to Predict Acute Kidney Injury After Surgery in Critically Ill Patients

Purpose Programmed cell death protein 1 (PD-1) and programmed death-ligand 1 (PD-L1) have been detected in injury kidney. However, their expressions are unclear in mice kidneys under renal ischemia-reperfusion injury (IRI). In this study, we would observe the expressions of PD-1 and PD-L1 in kidney tissues and analyze the association between the concentrations of PD-1 and PD-L1 in mouse kidney homogenate and the corresponding concentrations of soluble PD-1 (sPD-1) and soluble PD-L1 (sPD-L1) in plasma after renal IRI. Further, we explored the predictive value of sPD-1 and sPD-L1 for acute kidney injury (AKI) in high-risk patients after surgery. Methods This study established an AKI model induced by IRI in mice. Plasma, kidney samples, and homogenate were collected 0h, 24h, and 48h after surgery for immunohistochemistry and enzyme-linked immunosorbent assay. Then, we continuously enrolled 88 AKI high-risk patients who underwent noncardiac surgery. The biomarkers, including sPD-1, sPD-L1, and urine neutrophil gelatinase-associated lipocalin (NGAL), tissue inhibitor of metalloproteinase-2 (TIMP-2), insulin-like growth factor-binding protein 7 (IGFBP7), were detected immediately after surgery. Results Our data revealed the concentrations of PD-1 and PD-L1 in kidney homogenate, and sPD-1 and sPD-L1 in plasma significantly increased at 0h, 24h, and 48h after IRI. A positive association was found between PD-1 and sPD-1 (r = 0.774, p < 0.001), and between PD-L1 and sPD-L1 (r = 0.881, p < 0.001). Compared to NGAL, [TIMP-2]*[IGFBP7], sPD-1 and sPD-L1 showed better predictive abilities for AKI with an area under the ROC curve of 0.856 (95% confidence interval [CI]: 0.825–0.958, p < 0.001) and 0.906 (95% CI: 0.764–0.921, p < 0.001). Conclusion The increased expressions of PD-1 and PD-L1 in kidneys under IRI suggested they may play essential roles in AKI development. sPD-1 and sPD-L1 can indirectly reflect the expressions of PD-1 and PD-L1 in kidneys, respectively. sPD-1 and sPD-L1 showed excellent predictive ability for AKI in high-risk patients.
Application of User-centered iNPD Method

The advantages and disadvantages of traditional and modern patterns in the process of product design were analyzed.The user-centered iNPD process was introduced and the four stages in the product plan process were emphasized based on the analysis.The importance of product concept design was put forward.An example of book shelf design based on the theory was given to explain this innovative design.
Necessary Step From "ICT" to "LTK" in Foreign Language Teaching and Learning

: When we speak about new technologies and education, the first thing that comes to our mind is information and communications technologies or ICT. Technological progress and development of information systems have inevitably led to the incorporation of ICT in the teaching-learning methodologies. With the use of new digital educational content in our classrooms, temporal and spatial boundaries of those two processes have been transcended. However, teachers of foreign languages face the fact that the incorporation of ICT in the teaching procedure doesn’t imply necessary improvement of knowledge and language skills of students. Therefore, it is essential to develop digital competences of both teachers and students, which will further ensure adjustment of teaching-learning process to modern circumstances. Hence, it is necessary to introduce and apply a new concept in the teaching methodology - “LTK - Learning Technologies Knowledge“, which emphasizes the application of digital or virtual materials for the enhancement of teaching, learning and assessment. In this paper, we will propose certain useful guidelines and tools that can ensure proper application of ICT in our educational environment, thus improving students’ knowledge and skills while learning a foreign language.