Miracle-dz/UniFAR-FTUs huggingface dataset:
Similarities and Distinctions between Cortical Neural Substrates That Underlie Generation of Malevolent Creative Ideas

Abstract Creativity can be driven by negative intentions, and this is called malevolent creativity (MC). It is a type of creativity that serves antisocial purposes and deliberately leads to harmful or immoral results. A possible classification indicates that there are three kinds of MC in daily life: hurting people, lying, and playing tricks. This study aimed to explore similar and distinct neural substrates underlying these different kinds of MC idea generation. The participants were asked to perform different MC tasks, and their neural responses were recorded using a functional near-infrared spectroscopy device. The findings revealed that most regions within the prefrontal and temporal lobes [e.g., the right dorsolateral prefrontal cortex (rDLPFC), and right angular gyrus] were involved in the three MC tasks. However, the right frontopolar cortex (rFPC) was more activated and less coupled with the rDLPFC and right precuneus during the lying task than during the other tasks. Thus, rFPC may play an important role in constructing novel lies. In the lying task, individuals were more selfish and less compassionate. In the playing tricks and hurting people tasks, there was less neural coupling between the rDLPFC and the left inferior frontal gyrus/right inferior parietal lobule than that in the lying task. This may imply that selfish motivation is released when individuals try to ignore victims’ distress or generate aggressive tricks in hurting people or playing tricks tasks. These findings indicate that the three kinds of MC idea generation involve common cortical regions related to creative idea generation and moral judgment, whereas differences in cortical responses exist because of their unique features.
Molecular docking and physicochemical studies of 1,3-benzodioxole tagged Dacarbazine derivatives as an anticancer agent

Abstract Cancer, the biggest cause of death globally, remains a tough illness despite enormous advances in therapy. In the present study, 1,3-benzodioxole-tagged dacarbazine derivates were investigated as microtubule inhibitors in order to control cancer as microtubules are involved in cell proliferation. The tubulin protein was analyzed and its structure was validated by various protein validation tools. The binding potential of 1,3-benzodioxole-based dacarbazine-tagged derivatives with tubulin was checked using molecular docking software HEX 8.0 CUDA and AutoDock Vina. Swiss ADME online Web server and pkCSM are used for studying pharmacokinetic and pharmacological studies of compounds. The docking analysis ADME studies displayed that Compounds 1 and 2 bind effectively with the tubulin protein and showed potential properties to use as a potent anticancer drug.
Production and composition of extracellular polymeric substances by a unicellular strain and natural colonies of Microcystis: Impact of salinity and nutrient stress

Abstract The transfer of toxic cyanobacterial Microcystis blooms from freshwater to estuaries constitutes a serious environmental problem worldwide that is expected to expand in scale and intensity with anthropogenic and climate change. The formation and maintenance of Microcystis in colonial form is conditioned to the presence of extracellular polymeric substances (EPS). In this study, we attempted to better understand how the mucilaginous colonial form of Microcystis evolves under environmental stress conditions. In particular, we studied and compared the production and the composition of EPS fractions (attached and free) from natural colonies of a Microcystis bloom and from a unicellular M. aeruginosa strain under salinity and nutrient stress (representing a land‐sea continuum). Our results highlighted a greater production of EPS from the natural colonies of Microcystis than the unicellular one under nutrient and combined stress conditions dominated by the attached form. In comparison to the unicellular Microcystis, EPS produced by the colonial form were characterized by high molecular weight polysaccharides which were enriched in uronic acids and hexosamines, notably for the free fraction in response to increased salinities. This complex extracellular matrix gives the cells the ability to aggregate and allows the colonial cyanobacterial population to cope with osmotic shock.
The structure of a Lactobacillus helveticus chlorogenic acid esterase and the dynamics of its insertion domain provide insights into substrate binding

Chlorogenic acid esterases (ChlEs) are a useful class of enzymes that hydrolyze chlorogenic acid (CGA) into caffeic and quinic acids. ChlEs can break down CGA in foods to improve their sensory properties and release caffeic acid in the digestive system to improve the absorption of bioactive compounds. This work presents the structure, molecular dynamics, and biochemical characterization of a ChlE from Lactobacillus helveticus (Lh). Molecular dynamics simulations suggest that substrate access to the active site of LhChlE is modulated by two hairpin loops above the active site. Docking simulations and mutational analysis suggest that two residues within the loops, Gln145 and Lys164, are important for CGA binding. Lys164 provides a slight substrate preference for CGA, whereas Gln145 is required for efficient turnover. This work is the first to examine the dynamics of a bacterial ChlE and provides insights on substrate binding preference and turnover in this type of enzyme.
Impact of inner ear malformation and cochlear nerve deficiency on the development of auditory-language network in children with profound sensorineural hearing loss

Profound congenital sensorineural hearing loss (SNHL) prevents children from developing spoken language. Cochlear implantation and auditory brainstem implantation can provide partial hearing sensation, but language development outcomes can vary, particularly for patients with inner ear malformations and/or cochlear nerve deficiency (IEM&CND). Currently, the peripheral auditory structure is evaluated through visual inspection of clinical imaging, but this method is insufficient for surgical planning and prognosis. The central auditory pathway is also challenging to examine in vivo due to its delicate subcortical structures. Previous attempts to locate subcortical auditory nuclei using fMRI responses to sounds are not applicable to patients with profound hearing loss as no auditory brainstem responses can be detected in these individuals, making it impossible to capture corresponding blood oxygen signals in fMRI. In this study, we developed a new pipeline for mapping the auditory pathway using structural and diffusional MRI. We used a fixel-based approach to investigate the structural development of the auditory-language network for profound SNHL children with normal peripheral structure and those with IEM&CND under 6 years old. Our findings indicate that the language pathway is more sensitive to peripheral auditory condition than the central auditory pathway, highlighting the importance of early intervention for profound SNHL children to provide timely speech inputs. We also propose a comprehensive pre-surgical evaluation extending from the cochlea to the auditory-language network, showing significant correlations between age, gender, Cn.VIII median contrast value, and the language network with post-implant qualitative outcomes.
Attention-Guided Partial Domain Adaptation for Automated Pneumonia Diagnosis From Chest X-Ray Images

Deep neural networks (DNN) supported by multicenter large-scale Chest X-Ray (CXR) datasets can efficiently perform tasks such as disease identification, lesion segmentation, and report generation. However, the non-ignorable inter-domain heterogeneity caused by different equipment, ethnic groups, and scanning protocols may lead to dramatic degradation in model performance. Unsupervised domain adaptation (UDA) methods help alleviate the cross-domain discrepancy for subsequent analysis. Nevertheless, they may be prone to: 1) spatial negative transfer: misaligning non-transferable regions which have inadequate knowledge, and 2) semantic negative transfer: failing to extend to scenarios where the label spaces of the source and target domain are partially shared. In this work, we propose a classification-based framework named attention-guided partial domain adaptation (AGPDA) network for overcoming these two negative transfer challenges. AGPDA is composed of two key modules: 1) a region attention discrimination block (RADB) to generate fine-grained attention value via lightweight region-wise multi-adversarial networks. 2) a residual feature recalibration block (RFRB) trained with class-weighted maximum mean discrepancy (MMD) loss for down-weighing the irrelevant source samples. Extensive experiments on two publicly available CXR datasets containing a total of 8598 pneumonia (viral, bacterial, and COVID-19) cases, 7163 non-pneumonia or healthy cases, demonstrate the superior performance of our AGPDA. Especially on three partial transfer tasks, AGPDA significantly increases the accuracy, sensitivity, and F1 score by 4.35%, 4.05%, and 1.78% compared to recently strong baselines.
An overview of reproductive carrier screening panels for autosomal recessive and/or X‐linked conditions: How much do we know?

Reproductive carrier screening seeks to identify couples at a high risk of having offspring affected by autosomal recessive and X‐linked (XL) conditions. The aim of this paper is to provide a comprehensive overview of existing carrier screening panels by examining their gene content and characteristics, identifying the most common genes/conditions included in these panels, and analyzing their listed prices.
AI pitfalls and what not to do: mitigating bias in AI

Various forms of artificial intelligence (AI) applications are being deployed and used in many healthcare systems. As the use of these applications increases, we are learning the failures of these models and how they can perpetuate bias. With these new lessons, we need to prioritize bias evaluation and mitigation for radiology applications; all the while not ignoring the impact of changes in the larger enterprise AI deployment which may have downstream impact on performance of AI models. In this paper, we provide an updated review of known pitfalls causing AI bias and discuss strategies for mitigating these biases within the context of AI deployment in the larger healthcare enterprise. We describe these pitfalls by framing them in the larger AI lifecycle from problem definition, data set selection and curation, model training and deployment emphasizing that bias exists across a spectrum and is a sequela of a combination of both human and machine factors.
Quality of fresh and cryopreserved bovine sperm is reduced by BPA and BPF exposure

Bisphenol A (BPA) is an endocrine-disrupting compound, used as the key monomer of polycarbonate plastics and epoxy resins. BPA has been detected in both humans and farm animals and has been correlated with decreased sperm counts and motility. BPS and BPF are structural analogs of BPA and are increasingly being used in manufacturing as BPA substitutes. In this study, we aim to assess the direct outcomes of BPA, bisphenol S (BPS), and bisphenol F (BPF) exposure on bovine sperm parameters in vitro to elucidate how they affect sperm quality and fertilization potential, and to assess whether BPS and/or BPF are less harmful than BPA. Sperm from three or more bulls was obtained from either fresh samples or cryopreserved straws and exposed to 0.05 mg/mL of BPA, BPS, and BPF in vitro. After 4 h incubation, motility, capacitation, apoptosis/necrosis, and mitochondrial membrane potential levels were measured by computer-assisted sperm analysis or computational flow cytometry. Results showed that BPA exposure significantly reduced both fresh and cryopreserved sperm motility, capacitation, viability and mitochondrial membrane potential levels. Furthermore, BPF significantly decreased motility, capacitation and mitochondrial membrane potential in cryopreserved sperm only. BPS did not have any significant effects on any of the parameters measured. Our results suggest that BPA is the most harmful to sperm, while BPF is toxic under certain conditions, and BPS seems to be the least detrimental. Overall, this study provides an understanding of how the ubiquitous environmental chemicals, bisphenols, may impact male fertility even after ejaculation. Lay summary Bisphenol A (BPA) is a widespread man-made compound found in plastic products and the environment, and has been detected in human and animal biological fluids. BPA has been previously investigated for its negative effects on health, including its detrimental impacts on reproductive cells. Concerns over the use of BPA has led to its ban in some countries and has introduced the use of ‘BPA-free’ products, which typically contain substitutes such as bisphenols S and F (BPS and BPF). This current study investigates the effects of bull sperm exposure to BPA, BPS, and BPF to evaluate their impacts on sperm quality. After exposure, both BPA and BPF lowered sperm mobility, organelle function (by reducing mitochondrial activity), and overall survival; while BPS had no effects. Overall, this study shows that BPA and its common replacement, BPF, can both act on sperm directly, reducing their overall quality and function.
Promoting participation in physical activity through Snacktivity: A qualitative mixed methods study

Background Public health guidance acknowledges the benefits of physical activity of any duration. We have proposed a whole-day approach to promoting physical activity called Snacktivity™, which encourages frequent 2–5 minute ‘activity snacks’ of moderate-to-vigorous intensity. Methods Using repeated semi-structured interviews and a think aloud protocol, this study aimed to understand participants’ experiences of integrating Snacktivity™ into daily life, to provide insights to refine the delivery of Snacktivity™ interventions. Physically inactive adults recruited via primary care and a community health service engaged with an intervention to encourage Snacktivity™ over three weeks, which included using a Fitbit and linked mobile phone app (SnackApp). Participants took part in semi-structured interviews on two occasions during the intervention, with a sub-group participating in a think aloud study. Three study data sets were generated and independently explored using inductive thematic analysis, with findings combined into a single set of themes. Results Eleven adults participated in the interview study who were interviewed twice (total interviews completed n = 21, 1 participant declined the second interview), of whom six completed the think aloud study (total voice recordings n = 103). Three main themes emerged from the combined data; lived experience of participating in Snacktivity™, motivation for Snacktivity™ and experiences with the Snacktivity™ technology. Participants undertook a variety of activity snacks, utilising their environment, which they believed improved their psychological wellbeing. Participants were enthusiastic about Snacktivity™, with some stating that activity snacks were more accessible than traditional exercise, but perceived they were often prevented from doing so in the presence of others. Participants were mostly enthusiastic about using the Snacktivity™ technology. Conclusion Participants were able to incorporate Snacktivity™ into their lives, particularly at home, and found this approach acceptable. Participants felt they experienced health benefits from Snacktivity™ although barriers to participation were reported. This study offers insights for translating guidance into practice and supporting people to become more physically active.
Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees

Growth curve models are popular tools for studying the development of a response variable within subjects over time. Heterogeneity between subjects is common in such models, and researchers are typically interested in explaining or predicting this heterogeneity. We show how generalized linear mixed effects model (GLMM) trees can be used to identify subgroups with differently shaped trajectories in linear growth curve models. Originally developed for clustered cross-sectional data, GLMM trees are extended here to longitudinal data. The resulting extended GLMM trees are directly applicable to growth curve models as an important special case. In simulated and real-world data, we assess the performance of the extensions and compare against other partitioning methods for growth curve models. Extended GLMM trees perform more accurately than the original algorithm and LongCART, and similarly accurate as structural equation model (SEM) trees. In addition, GLMM trees allow for modeling both discrete and continuous time series, are less sensitive to (mis-)specification of the random-effects structure and are much faster to compute.
DSLOT-NN: Digit-Serial Left-to-Right Neural Network Accelerator

We propose a Digit-Serial Left-tO-righT (DSLOT) arithmetic based processing technique called DSLOT-NN with aim to accelerate inference of the convolution operation in the deep neural networks (DNNs). The proposed work has the ability to assess and terminate the ineffective convolutions which results in massive power and energy savings. The processing engine is comprised of low-latency most-significant-digit-first (MSDF) (also called online) multipliers and adders that processes data from left-to-right, allowing the execution of subsequent operations in digit-pipelined manner. Use of online operators eliminates the need for the development of complex mechanism of identifying the negative activation, as the output with highest weight value is generated first, and the sign of the result can be identified as soon as first nonzero digit is generated. The precision of the online operators can be tuned at runtime, making them extremely useful in situations where accuracy can be compromised for power and energy savings. The proposed design has been implemented on Xilinx Virtex-7 FPGA and is compared with state-of-the-art Stripes on various performance metrics. The results show the proposed design presents power savings, has shorter cycle time, and approximately 50% higher OPS per watt.
Kubo formula for dc conductivity: Generalization to systems with spin-orbit coupling

We revise the Kubo formula for the electric dc conductivity in the presence of spin-orbit coupling (SOC). We discover that each velocity operator that enters this formula differs from $\partial H/\partial \boldsymbol p$, where $H$ is the Hamiltonian and $\boldsymbol p$ is the canonical momentum. Moreover, we find an additional contribution to the Hall dc conductivity from noncommuting coordinates that is missing in the conventional Kubo-Streda formula. This contribution originates from the"electron-positron"matrix elements of the velocity and position operators. We argue that the widely used Rashba model does in fact provide a finite anomalous Hall dc conductivity in the metallic regime (in the noncrossing approximation) if SOC-corrections to the velocity and position operators are properly taken into account. While we focus on the response of the charge current to the electric field, linear response theories of other SOC-related effects should be modified similarly.
MultiCaM-Vis: Visual Exploration of Multi-Classification Model with High Number of Classes

Visual exploration of multi-classification models with large number of classes would help machine learning experts in identifying the root cause of a problem that occurs during learning phase such as miss-classification of instances. Most of the previous visual analytics solutions targeted only a few classes. In this paper, we present our interactive visual analytics tool, called MultiCaM-Vis, that provides \Emph{overview+detail} style parallel coordinate views and a Chord diagram for exploration and inspection of class-level miss-classification of instances. We also present results of a preliminary user study with 12 participants.
Analysis of Randomized Householder-Cholesky QR Factorization with Multisketching

CholeskyQR2 and shifted CholeskyQR3 are two state-of-the-art algorithms for computing tall-and-skinny QR factorizations since they attain high performance on current computer architectures. However, to guarantee stability, for some applications, CholeskyQR2 faces a prohibitive restriction on the condition number of the underlying matrix to factorize. Shifted CholeskyQR3 is stable but has $50\%$ more computational and communication costs than CholeskyQR2. In this paper, a randomized QR algorithm called Randomized Householder-Cholesky (\texttt{rand\_cholQR}) is proposed and analyzed. Using one or two random sketch matrices, it is proved that with high probability, its orthogonality error is bounded by a constant of the order of unit roundoff for any numerically full-rank matrix, and hence it is as stable as shifted CholeskyQR3. An evaluation of the performance of \texttt{rand\_cholQR} on a NVIDIA A100 GPU demonstrates that for tall-and-skinny matrices, \texttt{rand\_cholQR} with multiple sketch matrices is nearly as fast as, or in some cases faster than, CholeskyQR2. Hence, compared to CholeskyQR2, \texttt{rand\_cholQR} is more stable with almost no extra computational or memory cost, and therefore a superior algorithm both in theory and practice.
Mind the Uncertainty: Risk-Aware and Actively Exploring Model-Based Reinforcement Learning

We introduce a simple but effective method for managing risk in model-based reinforcement learning with trajectory sampling that involves probabilistic safety constraints and balancing of optimism in the face of epistemic uncertainty and pessimism in the face of aleatoric uncertainty of an ensemble of stochastic neural networks.Various experiments indicate that the separation of uncertainties is essential to performing well with data-driven MPC approaches in uncertain and safety-critical control environments.
Quantum Measurements and Delays in Scattering by Zero-Range Potentials

Eisenbud–Wigner–Smith delay and the Larmor time give different estimates for the duration of a quantum scattering event. The difference is most pronounced in the case where the de Broglie wavelength is large compared to the size of the scatterer. We use the methods of quantum measurement theory to analyse both approaches and to decide which one of them, if any, describes the duration a particle spends in the region that contains the scattering potential. The cases of transmission, reflection, and three-dimensional elastic scattering are discussed in some detail.
Quantitative Analysis of Forecasting Models: In the Aspect of Online Political Bias

Understanding and mitigating political bias in online social media platforms are crucial tasks to combat misinformation and echo chamber effects. However, characterizing political bias temporally using computational methods presents challenges due to the high frequency of noise in social media datasets. While existing research has explored various approaches to political bias characterization, the ability to forecast political bias and anticipate how political conversations might evolve in the near future has not been extensively studied. In this paper, we propose a heuristic approach to classify social media posts into five distinct political leaning categories. Since there is a lack of prior work on forecasting political bias, we conduct an in-depth analysis of existing baseline models to identify which model best fits to forecast political leaning time series. Our approach involves utilizing existing time series forecasting models on two social media datasets with different political ideologies, specifically Twitter and Gab. Through our experiments and analyses, we seek to shed light on the challenges and opportunities in forecasting political bias in social media platforms.