Miracle-dz/UniFAR-FTUs huggingface dataset:
Unobtrusive Recognition of Socio-Affective Dynamics During Human Interactions Using Wearables and Smartphones

Interactions with other people have a huge influence in one’s well-being, mental health, cognition, and development [14]. The importance of interactions is evident in our everyday life: we teach, learn, converse, and more. According to a recent analysis, social isolation and loneliness are risk factors for increased mortality [18]. In order to effectively maintain and enhance our own lives through successful interaction with others, we rely on these three basic and interrelated human capacities: affect (i.e., feelings about ourselves and others), behavior (i.e., interacting with others) and cognition (i.e., thinking about and understanding other people) [20]. Recent developments in ubiquitous computing enable new approaches for unobtrusive and automatic monitoring of all these physiological variables in a human being that may serve as key markers of humans’ behavior and interactions. Several studies [23, 33] show that it is possible to understand the behavior of users via smart phones. Recent work [31] shows that wearable physiological sensors (i.e., EDA, HR) are good proxies for measuring emotions and mood the two main indicators of affect. Hence, the ubiquitous technologies enabled the unobtrusive tracking of humans socio-affective dynamics during interactions. Researchers in computer-vision community studied social dynamics during interactions focusing on how two social partners coordinate externally observable behaviors (i.e., patterns of gesture) [32]. Although this approach can easily measure behavioral cues from the distance, behavioral cues alone are easier to control and may not always be congruent with the internal affective experience. Psychologists, on the other hand, suggest that our feelings, thoughts and behavior towards people around us are influenced by those people themselves this is studied by social psychology [20]. Therefore, several studies from psychology suggest that interactions may be better understood when the physiological dynamics of all people interacting to each other are known [27]. Physiological responses are externally responsive to in some cases dependent on the nervous system of others [5, 16, 17, 21, 24, 25, 27–29]. In fact, students performance in class is better understood when teachers performance is considered as well [9]. Similarly, children’s engagement is better understood considering also the engagement of the adult [17].
Long term outcomes in diabetic patients treated with atherectomy for peripheral artery disease.

BACKGROUND The prevalence of diabetes has increased significantly in well-developed countries during the last decade and it continues to grow. Diabetes increases the risk of restenosis in patients treated percutaneously for peripheral artery disease. The present study sought to compare outcomes of atherectomy treatment in diabetic (DM) vs. non-diabetic (nDM) patients suffering from peripheral artery disease. METHOD Between 2008 and 2012, 204 revascularization atherectomy procedures were performed on arteries of the lower extremities. The endpoints included target lesion revascularization (TLR), amputation and death. The type of atherectomy (excisional-soft plaque, orbital-calcified plaque, with active aspiration - with a thrombus) was left to operator discretion. RESULTS This study contains 132 DM (66% male, age 68 ± 11.2 years) and 72 nDM (63% male, age 75 ± 11.3 years) subjects. DM were younger but had a higher prevalence of coronary artery disease (DM: 91% vs. nDM: 62%, p < 0.0001) and end-stage renal disease (DM: 22% vs. nDM: 2.5%, p < 0.0001). There were no differences in critical limb ischemia between the groups (DM: 21% vs. nDM: 12%, p = 0.13). Mean time of follow-up was 384 and 411 days in DM and nDM, respectively (p = 0.43). There were no significant differences in TLR (DM: 15.2% vs. nDM: 22.2%, p = 0.249), amputations (DM: 3.0% vs. nDM: 1.5%, p = NS) or death rates (DM: 2.2% vs. nDM: 2.7%, p = NS). Kaplan-Mayer analysis showed no significant differences between the groups in the time to TLR, amputation or death. CONCLUSIONS Plaque modification with adjusted atherectomy appears to have similar outcomes in diabetic as well as in non-diabetic patients. Nonetheless, a randomized study would be warranted to confirm the findings of the current study.
Acetylation of ACAP4 regulates CCL18-elicited breast cancer cell migration and invasion

Tumor metastasis represents the main causes of cancer-related death. Our recent study showed that chemokine CCL18 secreted from tumor-associated macrophages regulates breast tumor metastasis, but the underlying mechanisms remain less clear. Here, we show that ARF6 GTPase-activating protein ACAP4 regulates CCL18-elicited breast cancer cell migration via the acetyltransferase PCAF-mediated acetylation. CCL18 stimulation elicited breast cancer cell migration and invasion via PCAF-dependent acetylation. ACAP4 physically interacts with PCAF and is a cognate substrate of PCAF during CCL18 stimulation. The acetylation site of ACAP4 by PCAF was mapped to Lys311 by mass spectrometric analyses. Importantly, dynamic acetylation of ACAP4 is essential for CCL18-induced breast cancer cell migration and invasion, as overexpression of the persistent acetylation-mimicking or non-acetylatable ACAP4 mutant blocked CCL18-elicited cell migration and invasion. Mechanistically, the acetylation of ACAP4 at Lys311 reduced the lipid-binding activity of ACAP4 to ensure a robust and dynamic cycling of ARF6-ACAP4 complex with plasma membrane in response to CCL18 stimulation. Thus, these results present a previously undefined mechanism by which CCL18-elicited acetylation of the PH domain controls dynamic interaction between ACAP4 and plasma membrane during breast cancer cell migration and invasion.
Smartphone-sensors Based Activity Recognition Using IndRNN

Human activity recognition based on the smartphone sensors has the potential to impact a wide range of applications such as healthcare, smart home, and remote monitoring. For simple activities like "Sit" and "Walk", it can be distinguished relatively easily. However, for similar activities with respect to transportations, such as "Train", "Bus" and "Subway", it still remains an open problem. To tackle this problem, this paper uses the recently proposed Independently Recurrent Neural Network (IndRNN) to process data of different lengths in order to capture the temporal patterns at different granularities. The unique property of IndRNN in terms of the gradient propagation allows it to construct deep RNN networks and extract good features. The proposed method has been applied and submitted to SHL recognition challenge as "UOW_AMRL".
Spontaneous Discriminative Response to the Biological Motion Displays Involving a Walking Conspecific in Mice

Recent translational studies using mice have contributed toward elucidating the neural, genetic, and molecular basis of social communication deficits. Nevertheless, many components of visual processes underlying mice sociality remain unresolved, including perception of bodily-movement. Here, we aimed to reveal the visual sensitivity of mice to information on bodily motion using biological motion displays depicted by simple geometric dots. We introduced biological motions extracted from walking mice vs. corresponding meaningless scrambled motions, in which the spatial configurations of each path of dots were shuffled. The apparatus was a three-chambered box with an opening between the chambers, and each side chamber had a monitor. We measured the exploration time of mice within the apparatus during the test, with two types of displays being presented. Mice spent more time in the chamber with the scrambled motion displays, indicating that animals spontaneously discriminated stimuli, with the scrambled motion being relatively novel. Furthermore, mice might have detected socially familiar cues from the biological motion displays. Subsequent testing revealed that additional mice showed no bias to the static versions of the stimuli used in the Movie test. Thus, we confirmed that mice modulated their behavior by focusing on the motion information of the stimuli, rather than the spatial configurations of each dot. Our findings provide a new perspective on how visual processing contributes to underlying social behavior in mice, potentially facilitating future translational studies of social deficits with respect to genetic and neural bases.
Accelerating Urban Science by Crowdsensing with Civil Officers

We present how crowdsensing with civil officers contribute to collect larger amount of urban data with high spatial-temporal coverage, and enable to understand urban features by analyzing the data. We implemented a crowdsensing system which fits to daily city management, and conducted long-term experiment with garbage section of Fujisawa city, Japan. Based on analysis of collected data, we show that our system provide fine-grained urban data compared to crowdsensing with citizens with improving efficiency of city management. In addition, we analyze our dataset with demographic urban data, and clarified the relationship between pattern of uncollectible garbage thrown and features of resident area.
Factors influencing clinical decision making used by mental health nurses to provide provisional diagnosis: A scoping review

Medical diagnosis has traditionally been the role of medical officers. However, mental health nurses working in crisis/emergency settings within Australia are expected to provide a provisional diagnosis postassessment of a consumer. There is limited literature and understanding how mental health nurses develop a provisional diagnosis. In this scoping review, we aimed to first identify and describe the clinical decision-making processes used by mental health nurses across a variety of clinical settings. Second, we sought to explore the factors influencing mental health nurse's diagnostic practice in a variety of settings. Literature was searched using CINAHL (EBSCOhost), PubMed, and ProQuest. Peer-reviewed literature published between 2007 and 2017 was used for this scoping review. Two major themes were identified: clinical decision making (CDM) in mental health nursing and diagnostic practice in nursing. A combination of clinician, environmental, and patient factors were found to have influenced CDM. Furthermore, mental health nurses rely heavily on tacit knowledge when making clinical decisions. Little is known about the use of diagnostic practice in mental health nursing in Australia; however, the limited literature revealed an overlap between the factors which influence CDM and diagnostic practice, respectively. Further research is needed into the use of diagnostic practice in mental health nursing to develop frameworks to assist with CDM pertaining to application of provisional diagnosis by mental health nurses working in assessment environments.
Safety and efficacy of a cardiologist-only approach to deep sedation for electrical cardioversion

Aims Electrical cardioversion is still the preferred method to restore sinus rhythm in patients with atrial fibrillation. The main disadvantage is that electrical cardioversion requires deep sedation, generally administered by anaesthesiologists, for safety concern. An exclusively cardiologic management of deep sedation should have the advantage to reduce resources and time consumed. Methods All consecutive patients admitted to our division with persistent atrial fibrillation or atrial flutter to undergo elective electrical cardioversion from June 2002 to December 2016 were included. The sedation protocol was managed only by cardiologists and involved the administration of a 5-mg bolus of midazolam, followed by increasing doses of propofol to achieve the desired sedation level. Exclusion criteria were strictly observed. Complications were recorded. A retrospective analysis on a deidentified database has been performed. Results A total of 1188 electrical cardioversions were scheduled in our centre. A total of 1195 patients were scheduled in our centre, of whom 1188 met inclusion criteria. Electrical cardioversion was performed in 1073 cases (90.3%). Electrical cardioversion was successful in restoring sinus rhythm in 1030 (96.0%) patients. Immediate recurrence of atrial fibrillation occurred in 89 patients (8.3%). 99/1073 (9.22%) patients underwent trans-oesophagel echocardiography before cardioversion. Deep sedation, according to our protocol, was effective in 100% of cases. Midazolam was administered at a dosage of 5 mg to all patients, while propofol was administered at a dosage ranging from 20 to 80 mg (25.1 ± 11.0 mg SD). No anaesthesia-related complications were observed, neither significant respiratory depression requiring intubation nor anaesthesiologist support. Conclusion The exclusively cardiological procedure of deep sedation seems to be safe and effective.
Emotion-Aware Systems for Promoting Human Well-being

Emotions play a fundamental role in people’s life. They influence work performance, learning outcomes [20], decision making [30], physical health and more [27]. Emotional well-being has been reported to be one of the key components of health [36]. Positive emotions help people to cope with adversity and increase the emotional well-being [32]. On the other hand, poor emotional well-being can lead to disorders such as anxiety, depression and chronic stress. A recent study from the World Health Organization (WHO) reported that globally, more than 300 million people suffer from depression, and 260 million suffer from anxiety disorders [37]. The organization found that such disorders cost the global economy 1 trillion dollars in lost productivity each year [38]. The WHO proposes among other countermeasures, the promotion and the awareness of emotional well-being and the prevention of mental health disorders as possible solutions to this critical social situation. Given the importance and the impact of emotion in people’s daily life, computer science researchers have explored the role of technology for understanding and improving emotional well-being. The idea of integrating emotions and more generally affect into technology, was presented for the first time in 1997 in a seminal work by Rosalind Picard [25]. This opened a new field of study called Affective Computing. The author envisioned that giving to "computers" a human-like ability to recognize and even express emotions, would allow not only a better experience in the usage of electronic devices but also enable a strong support in everyday human activity. Most of the initial efforts in the Affective Computing field were conducted as controlled studies in laboratory settings [31]. Even though these approaches have been fundamental for building knowledge in this field, they present some limitations. Findings in laboratory settings often do not reflect real situations and the models built in these cases are less robust to background noise typical of real environments. With the recent advances in mobile and wearable technologies the idea of Affective Computing has reached a new dimension. The ubiquitousness and pervasiveness of mobile devices allow researchers to continuously and unobtrusively gather people’s personal data in everyday life settings. This opens plenty of new possibilities for Affective Computing, enabling the creation of systems able to continuously capture large amounts of data in real environments, infer emotional state and react accordingly to ameliorate people’s life. This is moving the studies on emotional well-being from laboratory
Protein kinase C binding protein 1 inhibits hypoxia-inducible factor-1 in the heart.

AIMS Hypoxia-inducible factor-1 alpha (HIF-1α) is a key transcription factor responsible for the induction of genes that facilitate adaptation to hypoxia. To study HIF-1 signalling in the heart, we developed a mouse model in which an oxygen-stable form of HIF-1α can be inducibly expressed in cardiac myocytes, under the regulation of tetracycline. METHODS AND RESULTS Remarkably, expression of the transgene in mice generated two distinct phenotypes. One was the expected expression of HIF-regulated transcripts and associated changes in cardiac angiogenesis and contractility. The other was an unresponsive phenotype with much less expression of typical HIF-response genes and substantial expression of a zinc-finger protein, Protein Kinase C Binding Protein 1 (PRKCBP1). We have demonstrated that this second phenotype is due to an insertion of a fragment of DNA upstream of the PRKCBP1 gene that contains two additional canonical HIF binding sites and leads to substantial HIF binding, assessed by chromatin immunoprecipitation, and transcriptional activation. This insertion is found only in the FVB strain of mice that contributed the αMHC-tet binding protein transgene to these biallelic mice. In HEK293 cells transfected with oxygen-stable HIF-1α and PRKCBP1, we demonstrated inhibition of HIF-1 activity by a luciferase reporter assay. Using mouse primary cells and cell lines, we show that transfection with oxygen-stable HIF-1α and PRKCBP1 reduced expression of direct HIF-1 gene targets and that knockdown of PRKCBP1 removes that negative inhibition. Consistent with previous reports suggesting that PRKCBP1 modulates the chromatin landscape, we found that HL-1 cells transfected with oxygen-stable HIF-1α and PRKCBP1 have reduced global 5-methyl cytosine compared to HIF-1 alone. CONCLUSION We show genetic, transcriptional, biochemical, and physiological evidence that PRKCBP1 inhibits HIF activity. Identification of a new oxygen-dependent and previously unsuspected regulator of HIF may provide a target for new therapeutic approaches to ischaemic heart disease.
Learning to Embed Probabilistic Structures Between Deterministic Chaos and Random Process in a Variational Bayes Predictive-Coding RNN

This study introduces a stochastic predictive-coding RNN model that can learn to extract probabilistic structures hidden in fluctuating temporal patterns by dynamically changing the uncertainty of latent variables. The learning process of the model involves maximizing the lower bound on the marginal likelihood of the sequential data, which consists of two terms. The first one is the expectation of prediction errors and the second one is the divergence of the prior and the approximated posterior. The main focus in the current study is to examine how weighting of the second term during learning affects the way of internally representing the uncertainty hidden in the sequence data. The simulation experiment on learning a simple probabilistic finite state machine demonstrates that the estimation of uncertainty in the latent variable approaches zero at each time step and that the network imitates the probabilistic structure of the target sequences by developing deterministic chaos in the case of the high weighting. On the contrary, in the case of the low weighting, the estimate of uncertainty increases significantly because of developing a random process in the network. The analysis shows that generalization in learning is most successful between these two extremes. Qualitatively, the same property has been observed in a trail of learning more complex sequence data consisting of probabilistic transitions between a set of hand-drawn primitive patterns using the model extended with hierarchy.
Spectral Domain Optical Coherence Tomography Assessment of Macular and Optic Nerve Alterations in Patients with Glaucoma and Correlation with Visual Field Index

Introduction To evaluate the sectorial thickness of single retinal layers and optic nerve using spectral domain optic coherence tomography (SD-OCT) and highlight the parameters with the best diagnostic accuracy in distinguishing between normal and glaucoma subjects at different stages of the disease. Material and Methods For this cross-sectional study, 25 glaucomatous (49 eyes) and 18 age-matched healthy subjects (35 eyes) underwent a complete ophthalmologic examination including visual field testing. Sectorial thickness values of each retinal layer and of the optic nerve were measured using SD-OCT Glaucoma Module Premium Edition (GMPE) software. Each parameter was compared between the groups, and the layers and sectors with the best area under the receiver operating characteristic curve (AUC) were identified. Correlation of visual field index with the most relevant structural parameters was also evaluated. Results and Discussion All subjects were grouped according to stage as follows: Controls (CTRL); Early Stage Group (EG) (Stage 1 + Stage 2); Advanced Stage Group (AG) (Stage 3 + Stage 4 + Stage 5). mGCL TI, mGCL TO, mIPL TO, mean mGCL, cpRNFLt NS, and cpRNFLt TI showed the best results in terms of AUC according classification proposed by Swets (0.9 < AUC < 1.0). These parameters also showed significantly different values among group when CTRL vs EG, CTRL vs AG, and EG vs AG were compared. SD-OCT examination showed significant sectorial thickness differences in most of the macular layers when glaucomatous patients at different stages of the disease were compared each other and to the controls.
FUNN: Flexible Unsupervised Neural Network

Deep neural networks have demonstrated high accuracy in image classification tasks. However, they were shown to be weak against adversarial examples: a small perturbation in the image which changes the classification output dramatically. In recent years, several defenses have been proposed to solve this issue in supervised classification tasks. We propose a method to obtain robust features in unsupervised learning tasks against adversarial attacks. Our method differs from existing solutions by directly learning the robust features without the need to project the adversarial examples in the original examples distribution space. A first auto-encoder A1 is in charge of perturbing the input image to fool another auto-encoder A2 which is in charge of regenerating the original image. A1 tries to find the less perturbed image under the constraint that the error in the output of A2 should be at least equal to a threshold. Thanks to this training, the encoder of A2 will be robust against adversarial attacks and could be used in different tasks like classification. Using state-of-art network architectures, we demonstrate the robustness of the features obtained thanks to this method in classification tasks.
Reduced Cytosolic Calcium as an Early Decisive Cellular State in Parkinson’s Disease and Synucleinopathies

The more than 30-year-old Calcium hypothesis postulates that dysregulation in calcium dependent processes in the aging brain contributes to its increased vulnerability and this concept has been extended to Alzheimer’s disease and Parkinson’s disease. Central to the hypothesis is that increased levels of intracellular calcium develop and contributes to neuronal demise. We have studied the impact on cells encountering a gradual build-up of aggregated α-synuclein, which is a central process to Parkinson’s disease and other synucleinopathies. Surprisingly, we observed a yet unrecognized phase characterized by a reduced cytosolic calcium in cellular and neuronal models of Parkinson’s disease, caused by α-synuclein aggregates activating the endoplasmic calcium ATPase, SERCA. Counteracting the initial phase with low calcium rescues the subsequent degenerative phase with increased calcium and cell death – and demonstrates this early phase initiates decisive degenerative signals. In this review, we discuss our findings in relation to literature on calcium dysregulation in Parkinson’s disease and dementia.