Miracle-dz/UniFAR-FTUs huggingface dataset:
CSF levels of SNAP-25 are increased early in Creutzfeldt-Jakob and Alzheimer’s disease

Background Synaptosomal-associated protein 25 (SNAP-25) in cerebrospinal fluid (CSF) is an emerging synaptic biomarker for the early diagnosis of Alzheimer’s disease (AD). However, comprehensive studies investigating the marker in Creutzfeldt-Jakob disease (CJD) and in the differential diagnosis of neurodegenerative diseases are still lacking. Methods We developed a novel, sensitive ELISA for the measurement of SNAP-25 in CSF. In total, we analysed 316 patients from 6 diagnostic groups comprising patients with AD (n=96), CJD (n=55), Parkinson’s disease spectrum (n=41), frontotemporal lobar degeneration (n=25) and amyotrophic lateral sclerosis (n=24) and non-neurodegenerative control patients (n=75). Using receiver operating characteristic curve analysis, we analysed the differential diagnostic potential and compared the results with core AD biomarkers. Results SNAP-25 CSF concentrations were elevated in AD and CJD (p<0.0001) but not in the other neurodegenerative diseases. Increased levels were observed already at early AD and CJD stages (p<0.0001). In CJD, SNAP-25 levels correlated negatively with survival time (r=−0.33 (95% CI −0.57 to −0.04, p=0.02). For the discrimination of AD from all other diseases except CJD, we observed a good diagnostic performance for CSF SNAP-25 (area under the curve (AUC) 0.85) which was further improved by applying the ratio with CSF amyloid-β 1–42 (AUC 0.95). For CJD, we could demonstrate a strong differential diagnostic potential against all other groups including AD (AUC 0.97). Conclusion Using the novel established CSF SNAP-25 ELISA, we here demonstrate the applicability of SNAP-25 as an early synaptic biomarker for both AD and CJD with a possible prognostic value in patients with CJD.
Changes in physicochemical and anticancer properties modulated by chemically modified sugar moieties within sequence-related G-quadruplex structures

We systematically investigated the influence of locked nucleic acid (LNA), unlock nucleic acid (UNA), and 2’-O-methyl-RNA (2’-O-Me-RNA) residues on the thermal stability, structure folding topology, biological activity and enzymatic resistance of three sequence-related DNA G-quadruplexes. In order to better understand the mechanism of action of the studied modifications, a single-position substitution in the loops or G-tetrads was performed and their influence was analyzed for a total of twenty-seven modified G-quadruplex variants. The studies show that the influence of each modification on the physicochemical properties of G-quadruplexes is position-dependent, due to mutual interactions between G-tetrads, loops, and additional guanosine at 5’ or 3’ end. Nevertheless, the anticancer activity of the modified G-quadruplexes is determined by their structure, thus also by the local changes of chemical character of sugar moieties, what might influence the specific interactions with therapeutic targets. In general, UNA modifications are efficient modulators of the G-quadruplex thermodynamic stability, however they are poor tools to improve the anticancer properties. In contrast, LNA and 2’-O-Me-RNA modified G-quadruplexes demonstrated certain antiproliferative potential and might be used as molecular tools for designing novel G-quadruplex-based therapeutics.
Exploring policy options to combat illegal microapartments in Hong Kong

Illegal building can take multiple forms, including squatter settlements and illegal building extensions. Among the various forms of illegal building, illegal microapartments (IMAs), which take the form of unauthorised subdivided housing units, have recently aroused wide public concern in Hong Kong. On account of their unlawful nature, IMAs pose serious threats to the safety of local communities by undermining structural stability and fire safety in buildings. They may also adversely affect natural lighting and ventilation for building occupants. Fatal fires in buildings with IMAs in recent years have demonstrated the painful consequence of ignoring this issue in the city. Nonetheless, the problem of IMAs has seldom attracted scholarly attention around the world. In light of the seriousness of the IMA problem in Hong Kong, this study explores workable policy options for cracking down on the problem. It reviews policies or proposals in different jurisdictions to crack down on IMAs. Then a threeround policy Delphi study is carried out with a panel of stakeholders to identify and prioritise policy options for combating IMAs in Hong Kong. Various measures are proposed or identified by the panel members, ranging from increasing the frequency of building inspections and imposing stricter penalties on non-conforming owners to licensing IMAs for private renting. Among these options, stricter enforcement is considered the most workable option. The results of the policy Delphi study are discussed and policy recommendations are made.
Therapeutic Exercises and Modalities in Athletes With Acute Hamstring Injuries: A Systematic Review and Meta-analysis

Context: Hamstring strain is a common injury to the lower limbs. Early intervention in the acute phase aids with restoring hamstring function and prevents secondary related injury. Objective: To systematically review and summarize the effectiveness of exercise-based interventions combined with physical modalities currently used in athletes with acute hamstring injuries. Data Sources: Five databases (EMBASE, Medline, Cochrane Library, SPORTDiscus, and Web of Science) were searched from inception to July 2021. Study Selection: A total of 4569 studies were screened. Nine randomized controlled trials (RCTs) on the effect of therapeutic exercise programs with and without physical agents in athletes with acute hamstring injuries were identified for meta-analysis. Study Design: Systematic review and meta-analysis. Level of Evidence: Level 1. Data Extraction: The studies were screened, and the evidence was rated using the PEDro scale. Nine RCTs with PEDro scores ranging between 3 and 9 were included and extracted pain intensity, time to return to play (TTRTP), and reinjury rate in the study. Results: Loading exercises during extensive lengthening were shown to facilitate TTRTP at P < 0.0001 but did not prevent recurrence (P = 0.17), whereas strengthening with trunk stabilization and agility exercise did not reduce the duration of injury recurrence (P = 0.16), but significantly reduced the reinjury rate (P < 0.007) at a 12-month follow-up. The results of the stretching programs and solely physical modalities could not be pooled in the statistical analysis. Conclusion: The meta-analysis indicated that a loading program helps athletes to return to sports on a timely basis. Although strengthening with trunk stabilization and agility exercise cannot significantly reduce recovery time, the program can prevent reinjury. The clinical effects of stretching programs and pure physical modality interventions could not be concluded in this study due to limited evidence. PROSPERO Registration: CRD42020183035.
The amino acid sensor GCN2 controls red blood cell clearance and iron metabolism through regulation of liver macrophages

Significance In this study, we define a regulatory pathway controlling tissue-resident macrophages during stress through the translational control of a subset of messenger RNAs (mRNAs) and transcriptional reprogramming. Our data reveal that the GCN2-eIF2α pathway is enriched and activated in macrophages. Using mice devoid of GCN2, we show that lack of GCN2 interferes with the clearance of red blood cells (RBCs) during stress. We report an unexpected role of GCN2 in regulating lysosome maturation and phagocytosis of RBCs by macrophages during stress. Thus, we conclude that GCN2 regulates the clearance of RBCs through translational control of ATF4 mRNA in liver macrophages.
Using digital interventions to reduce digitalization-related stress: does it work?

Digitalization entails positive and negative consequences for employees. In a longitudinal, randomized control group design over 14 days (N = 95 participants), we piloted and expected each of three app-based interventions to positively influence general well-being, well-being related to information and communication technology (ICT) and recovery compared to the control group with no intervention. The meditation intervention significantly increased general well-being (satisfaction) and recovery (detachment) compared to the control group but did not reduce general stress. The cognitive-behavioural intervention significantly increased general well-being (less stress). The informational intervention, however, increased the general stress level. No intervention changed the level of ICT-specific well-being. Thus, classic stress interventions conveyed via ICTs (app-based) may be effective for addressing classic stress symptoms, but not yet for new forms of stress. Future research should investigate structural differences between classic stressors and new kinds of ICT-related stressors to identify starting points for new types of interventions.
Examining the role of road safety audits worldwide: exploring road safety expert’s opinions

Abstract Road safety audit (RSA) is a method used to proactively identify safety risks in road projects from planning, design, and construction stages. The effectiveness of the implementation of RSAs varies across the globe. This paper provides an understanding of road safety auditor’s opinions and attitudes by using data collected from an online questionnaire filled by 408 road safety experts with varying demographic and technical backgrounds. Results demonstrated that majority of respondents agreed that RSA is an effective tool to improve road safety. Compared to high-income countries, respondents from low- and middle-income countries had less knowledge about RSA and less access to RSA guidelines. The results also indicate that many road safety auditors have a misunderstanding of the independence requirement for conducting RSA. The results of this paper can be used to inform and develop improved guidance to improve RSA generally and promote RSA as an effective road safety engineering tool.
Infodemic of covid-19 and repercussions on the mental health of the elderly from São Paulo

ABSTRACT Objective: To characterize and identify depressive symptoms, anxiety, and stress associated with the COVID-19 Infodemic in the elderly from São Paulo. Method: Exploratory and cross-sectional study with the elderly in the capital of São Paulo who had internet access. The sociodemographic profile, the COVID-19 infodemic, depressive symptoms, stress, and anxiety were analyzed. Results: A total of 411 older people participated in the study. There was a predominance of women (76.4%), with higher education (57.9%), using private health services, and with little income variation. Older people were more exposed to news or information about COVID-19 on the internet (45.3%), followed by television (34.5%), and radio (11.4%). The average stress was 19.96 points; 33.1% had anxiety, and 39.7% had depressive symptoms. The greater the number of people living with the elderly, the greater the stress (p = 0.001) and anxiety (p = 0.02). The hours of exposure to information on the internet led to stress (p = 0.001), depressive symptoms (p = 0.02), and anxiety (p = 0.02) in the elderly. Conclusion: During the pandemic, exposure to information on the internet triggered anxiety, stress, and depressive symptoms in the elderly. The findings highlight the need for multi and interdisciplinary interventions to mitigate such repercussions on the elderly’s health.
Postural orthostatic tachycardia syndrome explained using a baroreflex response model

Patients with postural orthostatic tachycardia syndrome (POTS) experience an excessive increase in heart rate (HR) and low-frequency (∼0.1 Hz) blood pressure (BP) and HR oscillations upon head-up tilt (HUT). These responses are attributed to increased baroreflex (BR) responses modulating sympathetic and parasympathetic signalling. This study uses a closed-loop cardiovascular compartment model controlled by the BR to predict BP and HR dynamics in response to HUT. The cardiovascular model predicts these quantities in the left ventricle, upper and lower body arteries and veins. HUT is simulated by letting gravity shift blood volume (BV) from the upper to the lower body compartments, and the BR control is modelled using set-point functions modulating peripheral vascular resistance, compliance, and cardiac contractility in response to changes in mean carotid BP. We demonstrate that modulation of parameters characterizing BR sensitivity allows us to predict the persistent increase in HR and the low-frequency BP and HR oscillations observed in POTS patients. Moreover, by increasing BR sensitivity, inhibiting BR control of the lower body vasculature, and decreasing central BV, we demonstrate that it is possible to simulate patients with neuropathic and hyperadrenergic POTS.
Prognostic Value of Repeat Hepatectomy for Hepatocellular Carcinoma Patients

Background/Aim: The potential for repeat hepatectomy has been increasing in hepatocellular carcinoma (HCC) patients. Patients and Methods: To assess its significance, we examined the clinical features, surgical records, and survival of 279 HCC patients who underwent hepatectomy as primary treatment. Patients were divided into three groups: single hepatectomy without HCC relapse in 97 patients (Group 1), single hepatectomy with HCC relapse in 133 patients (Group 2) and repeat hepatectomy for HCC relapse in 49 patients (Group 3). Group 3 was divided into three subgroups. Multiple, larger size (>5 cm), and confluent nodular HCC. Results: Child–Pugh B patients were significantly higher in Group 2 (p<0.01). The prevalence of histological vascular involvement was significantly higher in Group 2 than in the other groups (p<0.01). Although Group 2 showed significantly poorer survival (p<0.01), the 10-year overall survival in Groups 1 and 3 was over 70% but not different between groups. The relapse-free survival period was significantly correlated with overall survival, and patients with a cancer-free period of over 36 months showed significantly better 10-year survival rates than those with a period of less than 36 months (p<0.01). Patients undergoing 3 or more repeat hepatectomies also showed good survival periods. Conclusion: Careful follow-up and proper decision-making in terms of repeat hepatectomy with multimodal treatments including novel chemotherapies upon assessment of postoperative tumor relapse are important in HCC patients to prolong survival.
SATO: spiking neural network acceleration via temporal-oriented dataflow and architecture

Event-driven spiking neural networks (SNNs) have shown great promise for being strikingly energy-efficient. SNN neurons integrate the spikes, accumulate the membrane potential, and fire output spike when the potential exceeds a threshold. Existing SNN accelerators, however, have to carry out such accumulation-comparison operation in serial. Repetitive spike generation at each time step not only increases latency as well as overall energy budget, but also incurs memory access overhead of fetching membrane potentials, both of which lessen the efficiency of SNN accelerators. Meanwhile, inherent highly sparse spikes of SNNs lead to imbalanced workloads among neurons that hurdle the utilization of processing elements (PEs). This paper proposes SATO, a temporal-parallel SNN accelerator that accumulates the membrane potential for all time steps in parallel. SATO architecture contains a novel binary adder-search tree to generate the output spike train, which decouples the chronological dependence in the accumulation-comparison operation. Moreover, SATO can evenly dispatch the compressed workloads to all PEs with maximized data locality of input spike trains based on a bucket-sort-based method. Our evaluations show that SATO outperforms the previous ANN accelerator 8-bit version of "Eyeriss" by 30.9× in terms of speedup and 12.3×, in terms of energy-saving. Compared with the state-of-the-art SNN accelerator "SpinalFlow", SATO can also achieve 6.4× performance gain and 4.8× energy reduction, which is quite impressive for inference.
DARPT: defense against remote physical attack based on TDC in multi-tenant scenario

With rapidly increasing demands for cloud computing, Field Programmable Gate Array (FPGA) has become popular in cloud datacenters. Although it improves computing performance through flexible hardware acceleration, new security concerns also come along. For example, unavoidable physical leakage from the Power Distribution Network (PDN) can be utilized by attackers to mount remote Side-Channel Attacks (SCA), such as Correlation Power Attacks (CPA). Remote Fault Attacks (FA) can also be successfully presented by malicious tenants in a cloud multi-tenant scenario, posing a significant threat to legal tenants. There are few hardware-based countermeasures to defeat both remote attacks that aforementioned. In this work, we exploit Time-to-Digital Converter (TDC) and propose a novel defense technique called DARPT (Defense Against Remote Physical attack based on TDC) to protect sensitive information from CPA and FA. Specifically, DARPT produces random clock jitters to reduce possible information leakage through the power side-channel and provides an early warning of FA by constantly monitoring the variation of the voltage drop across PDN. In comparison to the fact that 8k traces are enough for a successful CPA on FPGA without DARPT, our experimental results show that up to 800k traces (100 times) are not enough for the same FPGA protected by DARPT. Meanwhile, the TDC-based voltage monitor presents significant readout changes (by 51.82% or larger) under FA with ring oscillators, demonstrating sufficient sensitivities to voltage-drop-based FA.
A fast and low-cost comparison-free sorting engine with unary computing: late breaking results

Hardware-efficient implementation of sorting operation is crucial for numerous applications, particularly when fast and energy-efficient sorting of data is desired. Unary computing has been used for low-cost hardware sorting. This work proposes a comparison-free unary sorting engine by iteratively finding maximum values. Synthesis results show up to 81% reduction in hardware area compared to the state-of-the-art unary sorting design. By processing right-aligned unary bit-streams, our unary sorter is able to sort many inputs in fewer clock cycles.
Assessing the Causal Impact of Online Instruction due to COVID-19 on Students’ Grades and its aftermath on Grade Prediction Models

The COVID-19 pandemic forced many educational institutions to transition to online learning activities. This significantly impacted various aspects of students’ lives. Many of the studies aimed at assessing the impact of the online instruction on students’ wellbeing and performance have mainly focused on issues such as mental health. However, the impact on student grades—a key measure of student success—has been given little attention. The handful existing studies are either focused on primary schools—where the dynamics are different from higher education—or based on statistical correlations, which are usually not causally rigorous, therefore, prone to biased estimates due to various confounding variables. There are many variables associated with students’ grades, thus, to assess the causal impact of the online instruction on students’ grades, there is a need for a causally-grounded approach that can control for confounding variables. To that end, we use a causal tree to investigate the impact of online instruction on the grades of the general population as well as different demographic subgroups. Our analysis is based on the demographic and engagement data for the 2019 (offline/control) and 2020 (online/treatment) cohorts of 3 mandatory courses in an Australian university. For all 3 courses, our results show that for any given student in the population, the average grade they would have gotten, had they studied offline, reduced by 3.6%, 4.7%, and 14% respectively. Further analyses show that among students with similar level of (low) engagement with the virtual learning environment, the average grade international students would have gotten, had they studied face-to-face, reduced by 19.9%, 36.6%, and 46.9% more than their domestic counterparts despite having similar engagement for the 3 courses respectively. These subgroup disparities have the potential to exacerbate existing inequalities. Given the current concerns about algorithmic bias in learning analytics (LA), we trained grade prediction models with the data and investigated for algorithmic bias. Interestingly, we find that by simply changing citizenship status, a student gets a new predicted grade, entirely different from what was initially predicted given their actual citizenship status. This implies that researchers must be careful when building LA models on COVID-19 era data.
EBSP: evolving bit sparsity patterns for hardware-friendly inference of quantized deep neural networks

Model compression has been extensively investigated for supporting efficient neural network inference on edge-computing platforms due to the huge model size and computation amount. Recent researches embrace joint-way compression across multiple techniques for extreme compression. However, most joint-way methods adopt a naive solution that applies two approaches sequentially, which can be sub-optimal, as it lacks a systematic approach to incorporate them. This paper proposes the integration of aggressive joint-way compression into hardware design, namely EBSP. It is motivated by 1) the quantization allows simplifying hardware implementations; 2) the bit distribution of quantized weights can be viewed as an independent trainable variable; 3) the exploitation of bit sparsity in the quantized network has the potential to achieve better performance. To achieve that, this paper introduces the bit sparsity patterns to construct both highly expressive and inherently regular bit distribution in the quantized network. We further incorporate our sparsity constraint in training to evolve inherently bit distributions to the bit sparsity pattern. Moreover, the structure of the introduced bit sparsity pattern engenders minimum hardware implementation under competitive classification accuracy. Specifically, the quantized network constrained by bit sparsity pattern can be processed using LUTs with the fewest entries instead of multipliers in minimally modified computational hardware. Our experiments show that compared to Eyeriss, BitFusion, WAX, and OLAccel, EBSP with less than 0.8% accuracy loss, can achieve 87.3%, 79.7%, 75.2% and 58.9% energy reduction and 93.8%, 83.7%, 72.7% and 49.5% performance gain on average, respectively.
Applying deep learning approaches to mixed quantitative-qualitative analyses

We here verify whether a quantitative approach, i.e., a deep learning-based one, may be used to synthesize a model apt to perform specific qualitative analyses. To this aim, we leverage a previous contribution, where we approached the concrete problem of implementing a socio-historical classification toolchain for a collection of vernacular photos. In such a work, after individuating a corpus of vernacular photographs we devised the process that follows. First, we resorted to existing socio-historical categories derived from previous qualitative studies. Secondly, we involved the people included in the photos in the annotation process of a subset of the corpus of data. We then fine-tuned and deployed existing deep learning models to classify the entire corpus of data. Finally, we compared the results obtained with our approach to the ones obtained by a socio-historian. We hence here focus on the relationship between quantitative and qualitative methods considering the specific case of socio-historical analyses.