Miracle-dz/UniFAR-FTUs huggingface dataset:
Why do cells need oxygen? Insights from mitochondrial composition and function

Mitochondrial membrane‐embedded redox proteins are classically perceived as deterministic “electron transport chain” (ETC) arrays cum proton pumps; and oxygen is seen as an “immobile terminal electron acceptor. ” This is untenable because: (1) there are little free protons to be pumped out of the matrix; (2) proton pumping would be highly endergonic; (3) ETC‐chemiosmosis‐rotary ATP synthesis proposal is “irreducibly complex”/”non‐evolvable” and does not fit with mitochondrial architecture or structural/distribution data of the concerned proteins/components; (4) a plethora of experimental observations do not conform to the postulates/requisites; for example, there is little evidence for viable proton‐pumps/pH‐gradient in mitochondria, trans‐membrane potential (TMP) is non‐fluctuating/non‐trappable, oxygen is seen to give copious “diffusible reactive (oxygen) species” (DRS/DROS) in milieu, etc. Quite contrarily, the newly proposed murburn model's tenets agree with known principles of energetics/kinetics, and builds on established structural data and reported observations. In this purview, oxygen is needed to make DRS, the principal component of mitochondrial function. Complex V and porins respectively serve as proton‐inlet and turgor‐based water‐exodus portals, thereby achieving organellar homeostasis. Complexes I to IV possess ADP‐binding sites and their redox‐centers react/interact with O2/DRS. At/around these complexes, DRS cross‐react or activate/oxidize ADP/Pi via fast thermogenic one‐electron reaction(s), condensing to form two‐electron stabilized products (H2O2/H2O/ATP). The varied architecture and distribution of components in mitochondria validate DRS as (i) the coupling agent of oxidative reactions and phosphorylations, and (ii) the primary reason for manifestation of TMP in steady‐state. Explorations along the new precepts stand to provide greater insights on mitochondrial function and pathophysiology.
The Vitamin E Isoform α-Tocopherol is Not Effective as a Complementary Treatment in Cancer Treatment: A Systematic Review

Abstract The term vitamin E describes tocopherols and tocotrienols, whose chemical variations result in different biological activities including antioxidants. Neuroprotective effects of alpha-tocopherol against different toxins are assumed, therefore, it is discussed as a possible protective factor for adverse effects in cancer treatment. In July 2020, a systematic search was conducted searching five databases (Embase, Cochrane, PsychInfo, Cinahl, Medline) to find studies concerning the impact of α-tocopherol application and its potential harm on cancer patients. From 7546 search results, 22 publications referring to 20 studies with 1941 patients were included. Included patients were diagnosed with various cancer types and stages. Outcome variables were overall survival of cancer, symptom management of mucositis and chemotherapy-induced peripheral neuropathy (CIPN). The studies had different methodological qualities (mainly acceptable) and reported heterogeneous results: some reported significant improvement of mucositis and CIPN while others did not find changes concerning these endpoints. Due to heterogeneous results and methodical limitations of the included studies, a clear statement regarding the effectiveness of α-tocopherol as complementary treatment for cancer patients is not possible. Despite findings regarding reduction of oral side effects, usage of α-tocopherol during therapy must be discouraged because of potential negative influence on survival rates.
Blood management strategies in congenital Glanzmann thrombasthenia at a hematology referral center

Background Glanzmann thrombasthenia is associated with abnormalities in the glycoprotein IIb/IIIa receptor. This study, conducted at Ampang Hospital, Malaysia, aimed to assess outcomes of blood management strategies for Glanzmann thrombasthenia. Methods Ten patients with Glanzmann thrombasthenia aged 9 years (2009‒2018) were examined. Data on clinical characteristics, transfusion practices, and patient blood management were obtained from medical records. Patient blood management methods included parenteral iron, erythropoietin, hormonal pills, intrauterine progesterone contraceptive devices, tranexamic acid, and recombinant factor VIIa. Primary outcomes were hemoglobin levels and the proportion of patients who received blood transfusion. Secondary outcomes were morbidity and mortality. Results The median age at diagnosis was 8.2 years (range, 1‒15 yr). The female-to-male ratio was 91. Eight patients had type 2 disease (5‒20% of normal GPIIb/IIIa), and two patients had type 1 disease (normal GPIIb/IIIa <5%). All patients had iron deficiency. All female patients presented with significant menorrhagia. Other bleeding symptoms included epistaxis, spontaneous skin bruising, hemoptysis, gingival bleeding, knee hemarthrosis, and pelvic hematoma. No patient experienced life-threatening bleeding. Our patients had a mean hemoglobin level of 5.6 g/dL at diagnosis. All patients were optimized using non-transfusion methods as described above. Our patient had a current mean hemoglobin level of 11 g/dL. Approximately 70% (7/10) of patients did not experience receiving blood transfusions in the last 5 years. No patient experienced non-transfusion-related morbidities such as sepsis, thromboembolism, or cardiorespiratory events. Conclusion High cost, transfusion-related adverse events, and immunomodulation could be effectively prevented by avoiding unnecessary blood transfusions.
Contrasting gene‐level signatures of selection with reproductive fitness

Selection leaves signatures in the DNA sequence of genes, with many test statistics devised to detect its action. While these statistics are frequently used to support hypotheses about the adaptive significance of particular genes, the effect these genes have on reproductive fitness is rarely quantified experimentally. Consequently, it is unclear how gene‐level signatures of selection are associated with empirical estimates of gene effect on fitness. Eukaryotic data sets that permit this comparison are very limited. Using the model plant Arabidopsis thaliana, for which these resources are available, we calculated seven gene‐level substitution and polymorphism‐based statistics commonly used to infer selection (dN/dS, NI, DOS, Tajima's D, Fu and Li's D*, Fay and Wu's H, and Zeng's E) and, using knockout lines, compared these to gene‐level estimates of effect on fitness. We found that consistent with expectations, essential genes were more likely to be classified as negatively selected. By contrast, using 379 Arabidopsis genes for which data was available, we found no evidence that genes predicted to be positively selected had a significantly different effect on fitness than genes evolving more neutrally. We discuss these results in the context of the analytic challenges posed by Arabidopsis, one of the only systems in which this study could be conducted, and advocate for examination in additional systems. These results are relevant to the evaluation of genome‐wide studies across species where experimental fitness data is unavailable, as well as highlighting an increasing need for the latter.
Event-related driver stress detection with smartphones among young novice drivers

Abstract Complex and diverse driving situations can pose short-term stressors to novice drivers. Continuously detecting stress is essential for driver training, stress intervention, and the design of in-vehicle information systems. This study designed and validated a driver stress detection method at the event level based on machine learning algorithms and facial features captured with smartphones. Thirty young novice drivers completed two driving tasks containing eight events of two versions (neutral and stressful), with psychological, physiological, and facial data collected. Four combinations of input data types and six machine learning algorithms were used to detect stressful events. The KNN algorithm with facial plus individual profile features yielded the highest accuracy of 89.2%. Adding individual profile features can improve classification performance. Facial areas such as brow, eye, jaw, nose, and mouth were most sensitive to stress. This approach could provide more temporal-spatial information about the driver’s stress levels during the whole driving process. Practitioner Summary: This paper proposed a method to detect driver stress at the event level with smartphones. Models with facial plus individual profile features and the KNN algorithm had the most outstanding classification performance. The presented approach can serve as a tool for improving in-vehicle interaction system design when considering driver stress. Abbreviations: GSR: galvanic skin response; ECG: electrocardiography; HR: heart rate; HRV: heart rate variability; RGB: red green blue; NIR: near-infrared; IP: individual profile; DSI: driver stress inventory; APS: arousal predisposition scale; API: application programming interface; PPG: photoplethysmography; EDR: electrodermal response; PD: pupil diameter; SCL: skin conductance level; RF: random forest; KNN: k-nearest neighbour; LDA: linear discriminant analysis; QDA: quadratic discriminant analysis; SVML: support vector machines with the linear kernel; SVMP: support vector machines with the polynomial kernel; TP: true positive; TN: true negative; FP: false positive; FN: false negative; t-SNE: t-distributed stochastic neighbour embedding
Predictors of persistent inflammation in children with familial Mediterranean fever.

OBJECTIVES Persistent inflammation is an insidious feature of familial Mediterranean fever (FMF) that may cause chronic complications. This study aimed to investigate the predictors of persistent inflammation in children with FMF. METHODS The medical charts of 1077 paediatric FMF patients were retrospectively collected. The patients were divided into two groups: with and without subclinical inflammation. RESULTS A total of 133 (12%) patients had persistent inflammation. M694V homozygosity, colchicine resistance, positive family history for FMF, erysipelas-like erythema, leg pain, arthritis, chest pain, inflammatory comorbidities, early disease onset, high PRAS score, and long attack duration were established as independent predictors of persistent inflammation (P < .001, P < .001, P < .001, P < .001, P = 0.006, P < .001, P < .001, P = .014, P < .001, P < .001, and P < .001, respectively). However, gender, abdominal pain, fever, and attack frequency were not found to be independent risk factors for predicting persistent inflammation (P = .412, P = .531, P = .451, and P = .693, respectively). CONCLUSIONS M694V homozygosity, colchicine resistance, positive family history, erysipelas-like erythema, leg pain, arthritis, chest pain, inflammatory comorbidities, early disease onset, high activity score, and long attack duration may be predictors of persistent inflammation in FMF. These predictors may help clinicians suspect the occurrence of subclinical inflammation and should aid in better disease management in FMF.
Gym training shoe requirements in China and England

Background: Exercising in gyms is popular worldwide, among diverse populations and with different types of training applied. However, there is a lack of knowledge about general gym training footwear requirements of gym goers. Therefore, the purpose of this study was to survey the requisites of gym footwear. Methods: Our survey comprised of 935 regular gym goers from China and England who completed supervised hard copy questionnaires of gym footwear. The questionnaire included gym footwear wearing habits, importance of shoe properties, desired degree of shoe properties, shoe problems, and foot discomfort. Data was analysed using descriptive statistics for the total population and for subgroups of country, gender, and fitness level. Results: Notably, most gym goers did not use gym-specific footwear, but running shoes. Across all gym goers fit and comfort were regarded as the most important gym shoe features, followed by outward appearance and breathability. English males considered performance enhancing shoe properties more important and English females comfort ones, with no such gender differences in China. The plantar foot region was most prone to discomfort across the total population. In China the largest shoe problem was durability, whereas in England it was foot sweating. Gym goers typically desired a rather wide, soft cushioned, flexible, light gym shoe, also providing arch support and rather high traction. Conclusion: Footwear requirements were generally comparable across countries, genders, and fitness levels, although some differences were observed. Universally, shoe comfort and appearance features were more important than features directly enhancing athletic performance, reflecting the rather moderate movements of gym exercises. Our results provide comprehensive and evidence-based guidance for gym training footwear design and related research.
Supramammillary regulation of locomotion and hippocampal activity

Description Locomotion-related signals in the brain To calculate where we are in space, continuous knowledge of one’ s speed is necessary. How does the brain know how fast the body is traveling during locomotion? Using in vivo calcium imaging, electrophysiology, optogenetics, cell tracing, and histology, Farrell et al. identified neurons in the rodent supramammillary nucleus of the hypothalamus that encode future locomotor speed and potently drive locomotion when stimulated. Because these locomotor neurons have extensive axons in brain areas that support spatial navigation, this cell type distributes this information selectively to areas that require knowledge of speed. This nucleus is functionally positioned between input from a higher-order cognitive center and the downstream midbrain where locomotor nuclei reside. —PRS The activity of neurons in the supramamillary nucleus of the rodent hypothalamus is highly correlated with the speed of movement. Locomotor speed is a basic input used to calculate one’s position, but where this signal comes from is unclear. We identified neurons in the supramammillary nucleus (SuM) of the rodent hypothalamus that were highly correlated with future locomotor speed and reliably drove locomotion when activated. Robust locomotion control was specifically identified in Tac1 (substance P)–expressing (SuMTac1+) neurons, the activation of which selectively controlled the activity of speed-modulated hippocampal neurons. By contrast, Tac1-deficient (SuMTac1−) cells weakly regulated locomotion but potently controlled the spike timing of hippocampal neurons and were sufficient to entrain local network oscillations. These findings emphasize that the SuM not only regulates basic locomotor activity but also selectively shapes hippocampal neural activity in a manner that may support spatial navigation.
Workplace safety climate assessment based on behaviors and measurable indicators

Previous conceptualizations of safety climate present snapshots of the state of workplace safety. In reality, the factors of workplace safety climate are complex and need to be analyzed using a systemic approach. In this article, we expand on the customary concept of safety climate, and propose a systemic research framework that integrates four study fields: engineering, ergonomics, operation management, and technology management. In this framework, we define micro and macro safety climate concepts based on the study object and organizational hierarchy.
“We Came Together Out of Necessity”: A Latina Diaspora Group Engaging in Plática to Thrive with Dignity in Academia

In this article, five Latina faculty members and one Latina doctoral student engaged in Chicana feminist plática methodology to examine how creating and participating in a Latina diaspora group supports our personal and professional well-being. As Latina researchers/educators, situated in a predominantly White institution (PWI) in the Midwest, we collected narrative and discussion data centered around how our collaborative group provided a space to navigate academia and higher education. We analyzed our narratives and our analysis revealed three themes: (a) resisting the silos of academia through liminal spaces, (b) blurring our borders of the professional and personal through pláticas, and (c) disrupting hierarchies through la facultad. This article informs scholarly conversations about Latina/Chicana ways of knowing, and what it means to work in solidarity as women faculty of color within PWIs.
Tracking Lower Body 3D Kinematics using Three IMUs

This paper presents a Lie-group-based extended Kalman filter for estimating pelvis, thigh, shank, and foot kinematics during walking using three IMUs. The algorithm iterates through the prediction (kinematic equation), measurement (pelvis position pseudo-measurements, feet zero-velocity updates, and flat-floor assumption), and constraint update (hinged knee and ankle joints, max leg lengths), with hip, knee, and ankle angle correlation coefficients compared to a camera-based reference of 0.89±0.05, 0.86±0.04, 0.64±0.15. This work contributes toward the development of an ubiquitous remote gait monitoring system.
Force Control With Friction Compensation In A Pneumatic Gripper

Robots can grasp, even manipulate, objects with different shape, weight and size thanks to the their end-effectors. These are mostly constituted by two fingers, and are known as grippers. However, despite being quite simple for human beings, manipulation is not so straightforward to carry out on robotic systems. One of the main obstacles is the lack of reliable control methods: this is especially true for pneumatic grippers. Such devices are often mounted on industrial robots, though their behavior does not go beyond basic fully-open or fully-closed operations. This happens also as a consequence of the incapability of taking into account frictional effects limiting the pneumatic gripper performance. In this article, a new control strategy is delivered to solve this issue. The proposed strategy allows controlling the grasping force of a pneumatic gripper, without performance degradation due to friction. A pneumatic gripper was built and instrumented with several sensors to experimentally validate the proposed control strategy. The gripper was mechanically connected to a robotic arm and tested with different desired force profiles upon a wide range of force.
Forecasting very short-term wind power generation using deep learning, optimization and data decomposition techniques

Although accurate wind power prediction can improve the reliability, security and economic operation of a power system, the prediction task is complex due to the intermittent nature of wind speed its strong dependence on weather conditions. This paper proposes a novel framework, consisting of hybrid deep learning models, an optimization algorithm and a data decomposition technique, to improve the forecasting accuracy of ultra-short-term wind power generation. The data of the wind power generation collected from a real wind farm are preprocessed and decomposed using a variational mode decomposition (VMD). A deep-learning model (long short-term memory (LSTM) with dropout regularization) is designed to accurately predict the decomposed spectra. The hyper-parameters of the model are optimized by applying the grey-wolf optimization (GWO) algorithm to choose the best hyper-parameters. The combination of deep learning and optimization algorithm plays a key role in achieving better prediction accuracy. The effectiveness of the proposed framework is measured by applying it to two data sets, and the framework is compared with other forecasting models, such as a hybrid deep-learning and empirical wavelet transform (EWT) with LSTM. Comparison with experimental results demonstrates that the novel hybrid framework has the best prediction accuracy in forecasting ultra short-term wind power generation.
MRP performance effects due to lot size and planned lead time settings

The effects of lot size and planned lead time settings are evaluated in a shop producing assembled products with common components. Material requirements planning (MRP) logic is used for production planning. A framework for the analysis of delays within the production system is presented. These delays are evaluated using spreadsheet-based MRP software linked to a simulation package that emulates shopfloor activity. Results show that by selecting the proper lot sizes and then using the planned leadtimes to control delivery performance, the required inventory levels can be minimized.
Low Complexity Detection for Spatial Modulation Aided Sparse Code Division Multiple Access

Low-complexity detectors are conceivedfor the spatial modulation-aided sparse code-division multiple-access (SM-SCDMA). Firstly, the approximate message passing (AMP) algorithm is adapted for the detection of SM-SCDMA, which has less than 1% detection complexity of the state-of-the-art message passing-aided (MPA) detection albeit at a bit error ratio (BER) degradation. Therefore, inspired by the benefits of interference cancellation (IC) and the unique characteristics of SM, an enhanced AMP (EAMP) detector is proposed for mitigating BER degradation without increasing the detection complexity. Finally, the classic expectation propagation (EP) is intrinsically amalgamated with our EAMP detector, leading to a novel EAMP-EP detector, which further reduces the detection complexity of the EAMP algorithm. The three detectors are compared both in terms of their coded and uncoded BER, as well as complexity.
Synergetic Gait Prediction for Stroke Rehabilitation with Varying Walking Speeds

Lower Limb Exoskeletons (LLEs) are promising in gait rehabilitation for stroke survivors. In gait training of post-stroke patients with LLEs, one of the main challenges is how to generate appropriate gait patterns from the sound leg to the paretic leg for different patients with varying walking speeds. In this paper, we proposed a Synergetic Gait Prediction (SGP) model for rehabilitation LLEs with post-stroke patients, which can generate adaptive synergetic gait patterns for different patients with varying walking speeds. The proposed SGP model is based on Sequence-to-Sequence (Seq2Seq) neural networks with temporal attention mechanisms. In the training procedure of the proposed SGP model, a gait database with collected gait patterns from healthy subjects is employed to learn the parameters of SGP model. The SGP model takes current joint angles from the sound leg and a segment of observed history joint angles from both legs as input and predicts the future joint angles for the paretic leg. We compared the effectiveness of the SGP model with the Long Short Term Memory (LSTM) model, experimental results indicate that SGP model can generate synergetic gait patterns for different subjects via varying walking speeds with less prediction error.