Miracle-dz/UniFAR-FTUs huggingface dataset:
Genistein Modifies Hamster Behavior and Expression of Inflammatory Factors following Subchronic Unpredictable Mild Stress

Background: Previous studies have pointed to the protective role of genistein against stress adaptations although neuromolecular mechanisms are not yet fully known. With this work, we evaluated the influence of such a phytoestrogen on hamster behavioral and molecular activities following exposure to subchronic unpredictable mild stress. Methods: The motor behaviors of hamsters (n = 28) were analyzed using elevated plus maze (EPM) test, hole board (HB) test, and forced swim test (FST). In addition, neurodegeneration events were assessed with amino cupric silver stain, while expression variations of tropomyosin receptor kinase B (TrkB), nuclear factor kappa-B1 (NF-κB1), and heat shock protein 70 (Hsp70) mRNAs were highlighted in limbic neuronal fields via in situ hybridization. Results: Genistein accounted for increased motor performances in EPM and HB tests but reduced immobility during FST, which were correlated with diminished argyrophilic signals in some limbic neuronal fields. Contextually, upregulated Hsp70 and TrkB mRNAs occurred in hippocampal (HIP) and hypothalamic neuronal fields. Conversely, diminished NF-κB1 levels were mainly obtained in HIP. Conclusion: Hormonal neuroprotective properties of genistein corroborating anxiolytic and antidepressant role(s) through elevated expression levels of stress proteins and trophic factors may constitute novel therapeutic measures against emotional and stress-related motor performances.
Bowman‒Birk Inhibitor Suppresses Herpes Simplex Virus Type 2 Infection of Human Cervical Epithelial Cells

The Bowman‒Birk inhibitor (BBI), a protease inhibitor derived from soybeans, has been extensively studied in anti-tumor and anti-inflammation research. We recently reported that BBI has an anti-HIV-1 property in primary human macrophages. Because HSV-2 infection plays a role in facilitating HIV-1 sexual transmission, we thus examined whether BBI has the ability to inhibit HSV-2 infection. We demonstrated that BBI could potently inhibit HSV-2 replication in human cervical epithelial cells (End1/E6E7). This BBI-mediated HSV-2 inhibition was partially through blocking HSV-2-mediated activation of NF-κB and p38 MAPK pathways. In addition, BBI could activate the JAK/STAT pathway and enhance the expression of several antiviral interferon-stimulated genes (ISGs). Furthermore, BBI treatment of End1/E6E7 cells upregulated the expression of tight junction proteins and reduced HSV-2-mediated cellular ubiquitinated proteins’ degradation through suppressing the ubiquitin‒proteasome system. These observations indicate that BBI may have therapeutic potential for the prevention and treatment of HSV-2 infections.
Detecting racial bias in algorithms and machine learning

Purpose The online economy has not resolved the issue of racial bias in its applications. While algorithms are procedures that facilitate automated decision-making, or a sequence of unambiguous instructions, bias is a byproduct of these computations, bringing harm to historically disadvantaged populations. This paper argues that algorithmic biases explicitly and implicitly harm racial groups and lead to forms of discrimination. Relying upon sociological and technical research, the paper offers commentary on the need for more workplace diversity within high-tech industries and public policies that can detect or reduce the likelihood of racial bias in algorithmic design and execution. Design/methodology/approach The paper shares examples in the US where algorithmic biases have been reported and the strategies for explaining and addressing them. Findings The findings of the paper suggest that explicit racial bias in algorithms can be mitigated by existing laws, including those governing housing, employment, and the extension of credit. Implicit, or unconscious, biases are harder to redress without more diverse workplaces and public policies that have an approach to bias detection and mitigation. Research limitations/implications The major implication of this research is that further research needs to be done. Increasing the scholarly research in this area will be a major contribution in understanding how emerging technologies are creating disparate and unfair treatment for certain populations. Practical implications The practical implications of the work point to areas within industries and the government that can tackle the question of algorithmic bias, fairness and accountability, especially African-Americans. Social implications The social implications are that emerging technologies are not devoid of societal influences that constantly define positions of power, values, and norms. Originality/value The paper joins a scarcity of existing research, especially in the area that intersects race and algorithmic development.
Notes from the Field: Cronobacter sakazakii Meningitis in a Full-Term Neonate Fed Exclusively with Breast Milk — Indiana, 2018

Cronobacter sakazakii Meningitis in a Full-Term Neonate Fed Exclusively with Breast Milk — Indiana, 2018 Madhura Sundararajan, MPH1; Leslie A. Enane, MD2; Laurie A. Kidwell1; Ryan Gentry, MPH1; Stanley Danao1; Samina Bhumbra, MD2; Christopher Lehmann, MD3; Megan Teachout1; Jamie YeadonFagbohun1; Peter Krombach, MPH1; Betsy Schroeder, DVM4; Haley Martin5; Jonathan Winkjer5; Thomas Waltz5; Jonathan Strysko, MD4,5; Jennifer R. Cope, MD5
A Systematic Approach for Developing Cyber Physical Systems

Cyber physical systems (CPSs) are pervasive in our daily life from mobile phones to auto driving cars. CPSs are inherently complex due to their sophisticated behaviors and thus difficult to build. In this paper, we propose a systematic approach to develop CPSs with quality assurance throughout the development process. A CPS is abstracted and partitioned into a set of independent executing agents, where each agent is further refined into a set of behaviors. Each behavior is modeled with a high level Petri net, called behavior net. The overall behavior of an agent is modeled by an agent through composing individual behavior nets. Finally, the overall system behavior is modeled by a system net through integrating individual agent nets incrementally. Simulation and model checking can be performed on individual behavior nets, agent nets, and the final system net. The resulting system net is systematically mapped to behavior programs in Java, which are enhanced and extended with domain specific functionality. A set of property patterns based on behavior program is developed, which are used to generate runtime monitors to check behavior program executions. We demonstrate our approach using a multi-car parking system.
A variable projection method for block term decomposition of higher-order tensors

Higher-order tensors have become popular in many areas of applied mathematics such as statistics, scientiﬁc computing, signal processing or machine learning, notably thanks to the many possible ways of decomposing a tensor. In this paper, we focus on the best approximation in the least-squares sense of a higher-order tensor by a block term decomposition. Using variable projection, we express the tensor approximation problem as a minimization of a cost function on a Cartesian product of Stiefel manifolds. We present numerical experiments where variable projection makes a steepest-descent method approximately twice faster.
'Too Gay for Facebook'

Most US social media users engage regularly with multiple platforms. For LGBTQ+ people, this means making self-presentation decisions not just on one platform, but many. These choices are made in the face of sometimes-overlapping platform environments, which can have consequentially different norms, audiences, and affordances. Moreover, many LGBTQ+ users face high stakes in online self-presentation, due to the risk of stigmatization of their LGBTQ+ identity, increasing the importance of self-presentation decisions that enable them to achieve their goals and avoid stigmatization. This combination of environmental complexity and high stakes is not adequately accounted for in existing work on self-presentation, but doing so is important to support and understand the experiences of LGBTQ+ and other potentially stigmatized users. We adopt an ecological approach to an interview and cognitive mapping study of 20 LGBTQ+ social media users. We find that participants employ the platforms, audiences, affordances, and norms within what we call their "personal social media ecosystems" to avoid stigmatization while still allowing for expression of their LGBTQ+ identity and the flexibility to adjust their presentation over time.
Topic Modeling for Noisy Short Texts with Multiple Relations.

—Understanding contents in social networks by inferring high-quality latent topics from short texts is a signiﬁcant task in social analysis, which is challenging because social network contents are usually extremely short, noisy and full of informal vocabularies. Due to the lack of sufﬁcient word co-occurrence instances, well-known topic modeling methods such as LDA and LSA cannot uncover high-quality topic structures. Existing research works seek to pool short texts from social networks into pseudo documents or utilize the explicit relations among these short texts such as hashtags in tweets to make classic topic modeling methods work. In this paper, we explore this problem by proposing a topic model for noisy short texts with multiple relations called MRTM (Multiple Relational Topic Modeling). MRTM exploits both explicit and implicit relations by introducing a document-attribute distribution and a two-step random sampling strategy . Extensive experiments, compared with state-of-the-art topic modeling approaches, demonstrate that MRTM can alleviate the word co-occurrence sparsity and uncover high-quality latent topics from noisy short texts.
Interference Mitigation via Beam Range Biasing for 5G mmWave Coexistence with Incumbents

In this paper, we propose two interference mitigation techniques to enable the coexistence of fifth-generation new radio (5G-NR) with existing incumbent systems at millimeter frequencies. In particular, we propose to assign offsets, or bias values, to the base station beams such that users are discouraged to associate to the beams that are aligned with the incumbent receiver. We present a case study to evaluate the 5G uplink interference experienced at an existing incumbent in a dense urban city. Our results show that beam biasing provides better balance in 5G coverage and interference protection when compared to spatial exclusion zones.
Automatic prediction of intelligible speaking rate for individuals with ALS from speech acoustic and articulatory samples

Abstract Purpose: This research aimed to automatically predict intelligible speaking rate for individuals with Amyotrophic Lateral Sclerosis (ALS) based on speech acoustic and articulatory samples. Method: Twelve participants with ALS and two normal subjects produced a total of 1831 phrases. NDI Wave system was used to collect tongue and lip movement and acoustic data synchronously. A machine learning algorithm (i.e. support vector machine) was used to predict intelligible speaking rate (speech intelligibility × speaking rate) from acoustic and articulatory features of the recorded samples. Result: Acoustic, lip movement, and tongue movement information separately, yielded a R2 of 0.652, 0.660, and 0.678 and a Root Mean Squared Error (RMSE) of 41.096, 41.166, and 39.855 words per minute (WPM) between the predicted and actual values, respectively. Combining acoustic, lip and tongue information we obtained the highest R2 (0.712) and the lowest RMSE (37.562 WPM). Conclusion: The results revealed that our proposed analyses predicted the intelligible speaking rate of the participant with reasonably high accuracy by extracting the acoustic and/or articulatory features from one short speech sample. With further development, the analyses may be well-suited for clinical applications that require automatic speech severity prediction.
Recent advances in the discovery of small-molecule inhibitors of HIV-1 integrase

AIDS caused by the infection of HIV is a prevalent problem today. Rapid development of drug resistance to existing drug classes has called for the discovery of new targets. Within the three major enzymes (i.e., HIV-1 protease, HIV-1 reverse transcriptase and HIV-1 integrase [IN]) of the viral replication cycle, HIV-1 IN has been of particular interest due to the absence of human cellular homolog. HIV-1 IN catalyzes the integration of viral genetic material with the host genome, a key step in the viral replication process. Several novel classes of HIV IN inhibitors have been explored by targeting different sites on the enzyme. This review strives to provide readers with updates on the recent developments of HIV-1 IN inhibitors.
Motion Periods of Planet Gear Fault Meshing Behavior

Vibration sensors are, generally, fixed on the housing of planetary gearboxes for vibration monitoring. When a local fault occurred on the tooth of a planet gear, along with the system operating, the faulty tooth will mesh with the ring gear or sun gear at different positions referring to the fixed sensor. With consideration of the attenuation effect, the amplitudes of the fault-induced vibrations will be time-varying due to the time-varying transfer paths. These variations in signals are valuable information to identify the fault existence as well as the severity and types. However, the fault-meshing positions are time-varying and elusive due to the complicated kinematics or the compound motion behaviors of the internal rotating components. It is tough to accurately determine every fault meshing position though acquiring information from multi-sensors. However, there should exist some specific patterns of the fault meshing positions referring to the single sensor. To thoroughly investigate these motion patterns make effective fault diagnosis feasible merely by a single sensor. Unfortunately, so far few pieces of literature explicitly demonstrate these motion patterns in this regard. This article proposes a method to derive the motion periods of the fault-meshing positions with a faulty planet gear tooth, in which two conditions are considered: 1. The fault-meshing position initially occurs at the ring gear; 2. The fault-meshing position initially occurs at the sun gear. For each scenario, we derive the mathematical expression of the motion period in terms of rotational angles. These motion periods are, in essence, based on the teeth number of gears of a given planetary gearbox. Finally, the application of these motion periods for fault diagnosis is explored with experimental studies. The minimal required data length of a single sensor for effective fault diagnosis is revealed based on the motion periods.
How Deep Are Deep Gaussian Processes?

Recent research has shown the potential utility of Deep Gaussian Processes. These deep structures are probability distributions, designed through hierarchical construction, which are conditionally Gaussian. In this paper, the current published body of work is placed in a common framework and, through recursion, several classes of deep Gaussian processes are defined. The resulting samples generated from a deep Gaussian process have a Markovian structure with respect to the depth parameter, and the effective depth of the resulting process is interpreted in terms of the ergodicity, or non-ergodicity, of the resulting Markov chain. For the classes of deep Gaussian processes introduced, we provide results concerning their ergodicity and hence their effective depth. We also demonstrate how these processes may be used for inference; in particular we show how a Metropolis-within-Gibbs construction across the levels of the hierarchy can be used to derive sampling tools which are robust to the level of resolution used to represent the functions on a computer. For illustration, we consider the effect of ergodicity in some simple numerical examples.
Optimizing Over-The-Air Virtual Reality Broadcast Transmissions with Low-Latency Feedback

Virtual reality is one of the most challenging use cases in 5G due to the need for simultaneous support for high data rates, low-latency and high-reliability. In this work, we consider a radio resource efficient mechanism for the mass delivery of such content to a multitude of users with D2D augmented 5G broadcast. Based on detailed performance evaluations conducted using 5G system setting, it is shown that the optimized mechanism can provide significant gains in terms of cell-edge user throughput and resultant spectral requirements for broadcast. The performance gains are obtained due to the ability of the system to optimize the operating point, instead of targeting the worst user which has been the traditional approach for broadcast. The proposed mechanism could be an enabler for mitigating the key challenges currently foreseen for the broadcast of such immersive content.
Multiple sclerosis Molecular mimicry of an antimyelin HLA class I restricted T-cell receptor

Objective: To identify target antigens presented by human leukocyte antigen (HLA) – A * 02:01 to the myelin-reactive human T-cell receptor (TCR) 2D1, which was originally isolated from a CD8 1 T-cell clone recognizing proteolipid protein (PLP) in the context of HLA-A * 03:01, we employed a new antigen search technology. Methods: We used our recently developed antigen search technology that employs plasmid-encoded combinatorial peptide libraries and a highly sensitive single cell detection system to identify endogenous candidate peptides of mice and human origin. We validated candidate antigens by independent T-cell assays using synthetic peptides and refolded HLA:peptide complexes. A molecular model of HLA-A * 02:01:peptide complexes was obtained by molecular dynamics simulations. Results: We identified one peptide from glycerolphosphatidylcholine phosphodiesterase 1, which is identical in mice and humans and originates from a protein that is expressed in many cell types. When bound to HLA-A * 02:01, this peptide cross-stimulates the PLP-reactive HLA-A3-restricted TCR 2D1. Investigation of molecular details revealed that the peptide length plays a crucial role in its capacity to bind HLA-A * 02:01 and to activate TCR 2D1. Molecular modeling illustrated the 3D structures of activating HLA:peptide complexes. Conclusions: Our results show that our antigen search technology allows us to identify new candidate antigens of a presumably pathogenic,
Alterations in scapulothoracic and humerothoracic kinematics during the tennis serve in adolescent players with a history of shoulder problems

ABSTRACT The tennis serve generates high musculoskeletal loads at the shoulder complex, making athletes particularly vulnerable to chronic injuries, especially adolescent players. Chronic injuries are commonly related to altered scapular kinematics. This study explored the effects of a history of shoulder problems involving humerothoracic and scapulothoracic kinematics during the tennis serve at low speed in adolescent competitive players with and without a history of dominant shoulder problems. Totally, 28 adolescent tennis players were split into two groups, those with and those without a history of shoulder problems. Data on humeral and scapular kinematics relative to the thorax were collected using an electromagnetic system during slow velocity serves. The two groups’s humerothoracic and scapulothoracic 3D joint angles were compared both at the end of the cocking phase and at the end of the acceleration phase of the tennis serve. At the end of the cocking phase, the players with a 30 history of shoulder problems showed less humeral abduction and external rotation and more scapular upward rotation. This group also showed less humeral abduction at the end of the acceleration phase. Players with a history of shoulder problems adapted their humerothoracic and scapulothoracic orientations to preserve shoulder integrity during the tennis serve.
Application of Different Fourier Transform Infrared (FT-IR) Methods in the Characterization of Lime-Based Mortars with Oxblood

Organic compounds have frequently been added into lime mortars for property modifications, in order to satisfy various functional needs in building techniques. This study applies Fourier transform infrared (FT-IR) spectroscopy in transmission, reflection, and attenuated total reflection (ATR) modes to characterize lime-based mortar specimens containing oxblood, which has been used as additive as a common practice of long history in many parts of the world. The specimens were prepared basing upon a 19th-century Italian historic recipe, with the intention to have a better understanding on the possible characteristics of such mortars. Thermal analysis, color measurement, and static contact angle test were also used. After curing, the specimens show a distinctive dark-red color on the top surface, which is different from the bulk. Color measurements on the surface suggest that this color was formed at an early stage and was able to maintain stable for a relatively long period of time. Both transmission and reflection FT-IR confirm the preferential accumulation of proteins on the top surface, which should have induced their water repellency according to the static contact angle test. In addition, specimens show weaker calcite bands in FT-IR transmission, reflection, as well as ATR spectra; the pattern of ATR spectra after the thermal analysis to 500 °C suggests the formation of amorphous calcium carbonate, which is related to the presence of oxblood.
Chemical management of levodopa-induced dyskinesia in Parkinson’s disease patients

ABSTRACT Introduction: Levodopa-induced dyskinesias (LID) appears in more than 50% of Parkinson’s disease patients after 5 years of treatment and clinicians always have to ensure that there is a balance between the beneficial effect of the treatment and the potential complications. Areas covered: In this review, the authors discuss the treatment of LID. Treatment can be divided into strategies for preventing their occurrence, modification of dopaminergic therapy, and providing more continuous dopaminergic stimulation as well as the use of nondopaminergic drugs. Expert opinion: Amantadine is currently considered the most effective drug for the treatment of LID. Several compounds developed to target adenosine, adrenergic, glutamatergic, and serotonergic receptors have shown to significantly decrease dyskinesias in animal models. However, despite promising preclinical results, translation to clinical practice remains challenging and majority of these compounds failed to decrease LID in randomized controlled trials with moderate–to-advanced parkinsonian patients. Despite promising results with nondopaminergic drugs, treatment of dyskinesias is still challenging and largely due to their side effects. Future research should focus on developing treatments that can provide continuous dopaminergic delivery throughout the day in a noninvasive manner. Studies on the impact of the early administration of long-acting formulations of levo-3,4-dihydroxy-phenylalanine on dyskinesias are also necessary.