Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex dimorphism in response to targeted therapy and immunotherapy in non-small cell lung cancer patients: a narrative review

Background and Objective Multiple agents have been developed for treating non-small cell lung cancer (NSCLC). However, patients’ response to these therapies vary drastically, which indicates a need to tailor therapy. Sex is a readily usable clinical characteristic that has been shown to impact patients’ response to drugs. The main objective of this narrative review is to summarize the current state of knowledge, compiled from meta-analyses, on sex differences in treatment efficacy for targeted therapy and immunotherapy in NSCLC. We discuss the interplay of patient characteristics, both molecular and demographic, with sex on how they impact therapeutic response. Methods PubMed search was performed with the term “sex/gender differences” with currently FDA approved targeting therapy and immunotherapy agents in treating NSCLC. Key Content and Findings For targeted therapy, women tend to benefit more in terms of progression-free survival upon receiving first-generation anti-epidermal growth factor receptor (EGFR) treatment than men. On the other hand, there is an ongoing debate on sex differences in response to immunotherapy. Although preliminary, whether sex differences were observed depends on treatment settings, patient characteristics, and molecular features. Importantly, incorporating sex as a biological component in the biomarker discovery seems to reveal novel insights in immunotherapy response. Conclusions Taken together, sex differences in responding to standard care have been observed in clinical settings for NSCLC patients. A better understanding of sex-associated treatment response and the underlying biology will improve cancer prognosis and eliminate these sex differences.
An artificial intelligence system applied to recurrent cytogenetic aberrations and genetic progression scores predicts MYC rearrangements in large B‐cell lymphoma

Diffuse large B‐cell lymphoma (DLBCL), the most common type of non‐Hodgkin lymphoma, is characterized by MYC rearrangements (MYC R) in up to 15% of cases, and these have unfavorable prognosis. Due to cryptic rearrangements and variations in MYC breakpoints, MYC R may be undetectable by conventional methods in up to 10%–15% of cases. In this study, a retrospective proof of concept study, we sought to identify recurrent cytogenetic aberrations (RCAs), generate genetic progression scores (GP) from RCAs and apply these to an artificial intelligence (AI) algorithm to predict MYC status in the karyotypes of published cases. The developed AI algorithm is validated for its performance on our institutional cases. In addition, cytogenetic evolution pattern and clinical impact of RCAs was performed. Chromosome losses were associated with MYC‐, while partial gain of chromosome 1 was significant in MYC R tumors. MYC R was the sole driver alteration in MYC‐rearranged tumors, and evolution patterns revealed RCAs associated with gene expression signatures. A higher GPS value was associated with MYC R tumors. A subsequent AI algorithm (composed of RCAs + GPS) obtained a sensitivity of 91.4 and specificity of 93.8 at predicting MYC R. Analysis of an additional 59 institutional cases with the AI algorithm showed a sensitivity and specificity of 100% and 87% each with positive predictive value of 92%, and a negative predictive value of 100%. Cases with a MYC R showed a shorter survival.
CAA-YOLO: Combined-Attention-Augmented YOLO for Infrared Ocean Ships Detection

Infrared ocean ships detection still faces great challenges due to the low signal-to-noise ratio and low spatial resolution resulting in a severe lack of texture details for small infrared targets, as well as the distribution of the extremely multiscale ships. In this paper, we propose a CAA-YOLO to alleviate the problems. In this study, to highlight and preserve features of small targets, we apply a high-resolution feature layer (P2) to better use shallow details and the location information. In order to suppress the shallow noise of the P2 layer and further enhance the feature extraction capability, we introduce a TA module into the backbone. Moreover, we design a new feature fusion method to capture the long-range contextual information of small targets and propose a combined attention mechanism to enhance the ability of the feature fusion while suppressing the noise interference caused by the shallow feature layers. We conduct a detailed study of the algorithm based on a marine infrared dataset to verify the effectiveness of our algorithm, in which the AP and AR of small targets increase by 5.63% and 9.01%, respectively, and the mAP increases by 3.4% compared to that of YOLOv5.
The Skeptical Environmentalist: Measuring the Real State of the World

The subtitle gives the book away. It rehashes books such as Ronald Bailey's The True State of the Planet (Free Press, 1995). As Bjørn Lomborg tells us, the book's origin was a class he taught in 1997. The original Danish version appeared a mere year later —remarkably fast, given the delays of academic publishing. It shows, too. This survey of global environmental problems — food, forests, energy, water, pollution, biodiversity, global warming — reads like a compilation of term papers from one of those classes from hell where one has to fail all the students. It is a mass of poorly digested material, deeply flawed in its selection of examples and analysis.
Implicit Potential of Immersive Technologies Implementation in the Educational Process at the Universities: World Experience

: The article identiﬁes the implicit potential of immersive technologies implementation in in the educational space of universities around the world. The content of basic research concepts has been determined. The advantages and disadvantages of using immersive technologies have been analyzed. The achievements of the world’s universities have been clariﬁed: the use of immersive technologies in the professional training of the future archaeologists to perform work on archaeological excavations has been reﬂected; in training architects and engineers for computer modeling of any of the most complex projects; in the training of the future pilots to guide the landing of aircraft on the aircraft carrier; in training rescuers to extinguish ﬁres and rescue people; in the training of the future physicians for surgery or for experiments with hazardous chemicals. Emphasis is placed on the use of immersive technologies in the education of students with special educational needs to create inclusive learning environment, taking into account the needs and capabilities of each student. Based on the analysis of world experience in the use of immersive technologies in the educational space of universities, it was found that these technologies are used in the following areas: a) immersive learning technologies are actively used during distance learning, which allows, in particular during video conferencing to improve learning efﬁciency (University of British Columbia); b) to determine the level of empathy for the problem of homelessness, which allows to get the social experience of a person who becomes homeless (Central Paciﬁc Institute in Hawaii); c) to study the effects of ocean oxidation on coral reefs, to provide knowledge about the environmental problem and to develop negative emotions in students about human activities that harm the beautiful and pristine ecosystem (Punahou International University). It is generalized that the use of immersive technologies in the educational space of universities of the world is used not only in the process of professional training for various sectors of the economy to gain professional competencies, but also to gain social, emotional experience and to actualize environmental issues.
Effect of PET Size, Content and Mixing Process on the Rheological Characteristics of Flexible Pavement

The performance of asphalt binder reinforced with waste plastic polyethylene terephthalate (PET) was investigated. Penetration, ductility, softening point, and rotational viscosity tests were conducted to check the performance of the PET-reinforced pavement. The rheological properties of the binder were determined using amplitude sweep and frequency sweep tests and performance grade (PG) measurements of aged and unaged specimens. PET size, mix mechanism, and mix temperature significantly influenced the physical properties of the AB and the penetration index (PI). The size and content of PET had pronounced effects on the PI and softening point than the blending temperature. Increasing the size of PET particles from 75 to 150 μm and the content from 0% to 10% of the bitumen resulted in the reduction of the penetration and ductility values from 96 to 85 mm and 100 to 78 cm, respectively, whereas the softening point increased from 46 to 56.6 °C. As a result, the PI value of the binder increased, which indicates that the temperature susceptibility was improved. The addition of 10% PET increased the viscosity of the baseline bitumen by threefold upto a temperature of 135 °C and dropped it by fourfold when the temperature was raised to 165 °C. Increasing the PET from 0% to 10% and the temperature from 21.1 to 54.4 °C increased the critical strain value (LVER) by 96%.
GLTC: A Metro Passenger Identification Method Across AFC Data and Sparse WiFi Data

In this paper, we investigate an efficient way for identifying passengers in a metro system across two heterogeneous but complementary trajectory data sources: AFC data recording two points per trip about when and where a passenger enters or leaves the metro system, and WiFi data recording a few points passed by a passenger in the way of some of his/her trips. The identification result can help us to complete individuals’ mobility, and benefits to lots of services, e.g., individual route choice analysis, epidemic case detecting and so on. The problem is similar to calculate the similarity between two trajectories from two data sources, where a trajectory refers to a sequence of points where a passenger appeared in a metro system on observed days. However, due to the small location space in a metro network, large number of passengers with similar travel pattern, and so on, there are lots of trip overlaps or point co-occurrences between different passengers. That results in a large number of passengers mismatched by existing trajectory similarity measurement. To address the problem, this paper proposes a novel global-local correlation based trajectory similarity measurement GLTC. Specifically, GLTC first extracts all overlapping trip pairs of two trajectories by considering the spatiotemporal inclusions from global level. Then it gets the similarity by aggregating each overlapping trip pair’s local similarity, which is calculated by considering some data-driven insights helpful to uniquely identify a passenger (e.g., uneven passenger flow distribution in different cross-sections of a metro network, the number of trips in same travel pattern of a trajectory, and so on). We evaluate GLTC based on real-world data, and the experimental result shows that GLTC outperforms other baselines.
Analisis Deteksi Skema Modulasi Digital Single Carrier Dan Multi Carrier Pada Kanal Fading Dan AWGN

Pada penelitian ini, dilakukan simulasi dan analisis deteksi skema modulasi digital singlecarrier dan multi carrier. Metode multi carrier yang akan dianalisa adalah OFDM. Tujuan utamanya adalah dapat membedakan sinyal termodulasi single carrier dan multi carrier, dan dapat mendeteksi skema modulasi di masing-masing single carrier dan multi carrier. Pada simulasi, skema modulasi yang akan dideteksi adalah QPSK, 16QAM, dan 64QAM di mana sebelumnya sinyal akan melewati kanal fading terdistribusi Rayleigh dan AWGN. Metode yang digunakan pada ekstraksi ciri adalah metode spektrum dan statistik. Sedangkan pada proses klasifikasi digunakan metode Jaringan Syaraf Tiruan (JST-BP). Simulasi dilakukan dengan menggunakan tujuh ciri sebagai neuron masukkan dan enam tipe sinyal sebagai enam neuron keluaran. Hasil penelitian menunjukkan bahwa perubahan nilai learning rate, jumlah neuron, dan jumlah lapisan tersembunyi dapat mempengaruhi performansi JST-BP dalam aspek persentase akurasi. Pada fading Rayleigh dengan kecepatan 120 km/jam, sistem JST-BP menghasilkan persentase akurasi pengujian sebesar > 90% ketika kondisi SNR > 8 dB. Kata Kunci : Automatic Modulation Recognition (AMR), OFDM, Rayleigh Fading, Jaringan Syaraf Tiruan
Study of moisture content in leaves through regression analysis of terahertz images

Precision agriculture has evolved over the years to meet the growing demand for agricultural productivity with limited resources, and with it, the smart irrigation techniques are also gaining traction. Management of water is critical since it is one of the most significant components of the photosynthetic process and hence an indicator of crop health and yield. Due to the high sensitivity of Terahertz (THz) radiation towards the presence of water, in current work, the moisture content in leaves from a Capsicum annuum plant is approximated using THz time-domain imaging. To overcome the effect of approximations and limitations in theoretical models, this work aims to generalize the prediction of moisture content in plants by simulating drought stress in an un-watered plant through a detached leaf. This is achieved through analysis of time-lapsed THz imaging of several leaves by employing a machine learning approach. The THz images are processed to retrieve pixels corresponding only to the flat lamina without the veins because of their morphological differences. The process is repeated for all instances of images as the leaf dries up. For predicting the moisture content in the leaf, transmittance of the selected pixels at selected frequencies ranging from 0.4-2.1 THz are used to train supervised machine learning regression (SMLR) model. Standard error of estimate (SEE) used for performance analysis of Decision Tree, Random Forest and Support Vector regression models show that as the drought sets in and the leaves dry up, the prediction accuracy improves.
Psychological distress of COVID-19: using a positive psychology approach for mental health promotion in the general population

ABSTRACT Τhe present study aimed to generate evidence on the role of core elements of positive psychology interventions (PPIs) such as empathy, resilience, gratitude and hope in mitigating the psychological distress of the COVID-19 pandemic. Participants (N = 203) were recruited from the island of Ireland (mean age = 37.7 years, ± 13.2) and completed an online questionnaire including measures of resilience, hope, gratitude, empathy, depression, stress, anxiety and the subjective distress of COVID-19. A four-step hierarchical regression model was applied. Resilience showed positive significant correlations with gratitude, hope and empathy. Age, gender, presence/absence of a chronic health condition (CHC) and country of residence were not predictors of the subjective distress of COVID-19. Resilience and empathy were a negative and a positive predictor of the subjective distress respectively, while gratitude and hope had no predictive value in this model. Upon adding depression, stress and anxiety, the explained variance in scores of the distress of COVID-19 increased considerably from 16% to 55%. Individuals who experience higher levels of depression and anxiety without necessarily the presence of a CHC and regardless of age, gender, and country of residence, may be more susceptible to experience the subjective distress of COVID-19. These findings can be used to inform the design and delivery of PPIs either as a public health prevention measure or as a treatment programme within a broader context of a public mental health promotion strategy to tackle the psychological impact of this pandemic in adults of the general population.
The Fuzzy Kalman Filter of Damp Attitude Algorithm

Traditional inertial mechanized-platform uses velocities to damp the system attitude to improve the precision of attitude,when the system acceleration is small.Referring to the idea,this paper designeda damp Kalman filter in Strap-down Attitude Heading Reference System(AHRS). The new method makes use of 3-D accelerometer's measurements to estimate the system attitude,which is measured to compensate attitude errors.Because the acceleration affected the precision of filter directly,the Fuzzy adaptive system was presented.The Fuzzy Logic inputs are three accelerations and the output is to control the measurement noise covariance matrix.Simulations and experimental results prove that the damp algorithm can damp most of Schuler oscillation and Foucault oscillation,so that to assure the filter convergence and efficiently improve the precision of strap-down AHRS.
What Competencies Does a Community Occupational Therapist Need in Neurorehabilitation? Qualitative Perspectives

More than three million people in Chile suffer from neurological conditions, and many of these become permanent users of health services with a community approach. In this way, disciplinary competencies in this area are relevant. We seek to characterize the competencies for community occupational therapy intervention in neurorehabilitation. Using a qualitative approach, interviews were conducted with eighteen professionals and were analyzed using content analysis. The main results are associated with the competencies of knowledge associated with theoretical biomedical and community elements. Skills range from health evaluation and intervention on micro- and macrosocial levels. Attitude is also an important skill, stemming from personal and relational spheres. These findings suggest that interventions are essentially on a personal and microsocial level, focusing first on pathology and treatment, and later comprehending the interactions with a patient’s close social environment, such as family, schoolmates, and workmates and their physical environment at home, school, and the workplace. Although the final objective of community intervention is present in the discourse as being able to generate structural changes that favor well-being and social inclusion, concrete competencies are not appreciated on a macrosocial level.
Molecular Mechanisms Underlying the Pain-Relieving Effects of Extracorporeal Shock Wave Therapy: A Focus on Fascia Nociceptors

In recent years, extracorporeal shock wave therapy (ESWT) has received increasing attention for its potential beneficial effects on various bone and soft-tissue pathologies, yielding promising outcomes for pain relief and functional recovery. In fact, ESWT has emerged as an alternative, non-invasive, and safe treatment for the management of numerous musculoskeletal disorders, including myofascial pain syndrome (MPS). In particular, MPS is a common chronic painful condition, accounting for the largest proportion of patients affected by musculoskeletal problems. Remarkably, sensory innervation and nociceptors of the fascial system are emerging to play a pivotal role as pain generators in MPS. At the same time, increasing evidence demonstrates that application of ESWT results in selective loss of sensory unmyelinated nerve fibers, thereby inducing long-lasting analgesia. The findings discussed in the present review are supposed to add novel viewpoints that may further enrich our knowledge on the complex interactions occurring between disorders of the deep fascia including changes in innervation, sensitization of fascial nociceptors, the pathophysiology of chronic musculoskeletal pain of MPS, and EWST-induced analgesia. Moreover, gaining mechanistic insights into the molecular mechanisms of pain-alleviating effects of ESWT may broaden the fields of shock waves clinical practice far beyond the musculoskeletal system or its original application for lithotripsy.
Planning for a Healthy Aging Program to Reduce Sedentary Behavior: Perceptions among Diverse Older Adults

Reducing prolonged engagement in sedentary behavior is increasingly considered a viable pathway to older-adult health and continued functional ability. Community-based programs that aim to increase physical activity can improve programs’ acceptability by integrating older adults’ perspectives on sedentary behavior and healthy aging into their design. The purpose of this study was to better understand the perceptions of a diverse group of community-dwelling older adults regarding sedentary behavior and its influence on healthy aging. Six focus group discussions with forty-six participants took place across two senior centers in New York City. Self-report questionnaires about daily activity patterns, general health status, and typical sedentary behaviors were also completed by the participants and analyzed using descriptive statistics. The focus group discussions were audio-recorded, transcribed, and analyzed using inductive and deductive approaches and an ecological framework to identify salient themes. A qualitative analysis revealed that the participants were aware of the physical costs of engaging in prolonged sedentary behavior. However, many routine sedentary activities were perceived to be health-promoting and of psychological, cognitive, or social value. The insights gained can inform the development of senior-center programs and health-promotion messaging strategies that aim to reduce older adults’ sedentary behavior.
The Adaptive Fingerprint Localization in Dynamic Environment

Indoor localization service is an indispensable part of modern intelligent life, among which Wi-Fi based fingerprint localization system is popular in indoor positioning researches due to its advantages of low cost and widely deployment. However, Wi-Fi based localization system is susceptible to dynamic environment, and fingerprint collection and updating are time-consuming and labor-intensive. To address this problem, we propose a novel positioning framework based on multiple transfer learning fusion using Generalized Policy Iteration (GPI). Firstly, a 1-Dimension Convolutional Autoencoder (1-D CAE) is designed to extract features from one-dimensional fingerprint data; similar to Convolutional Neural Network (CNN), it can not only pay more attention to the information of different dimensions of fingerprints, but also compress redundant information and reduce noise. After that, Domain Adversarial Neural Network (DANN) and Passive Aggressive (PA) algorithm are fused to train localization model based on unlabeled fingerprint of target domain using the theory of GPI in offline stage. Finally, the model is fine-tuned with unlabeled fingerprints and few labeled fingerprints in daily online predictions to improve the performance of the localization system. Various evaluations in five typical scenarios validate the effectiveness of proposed algorithm in dynamic environment, with low tendency, easy recalibration, long-term stabilization high accuracy and so on.
Self-Study, Ideology, and Teacher’s Self-Knowledge in Guiding Curriculum Decisions

What a teacher believes and envisions as an educator has a profound influence on teaching and learning. This paper explores the importance of self-study and teacher’s ideology as a methodology to systematically assess the evolutionary nature of teacher’s ideology and its importance in envisioning teaching and learning in K-12 education. Ideology is used in this paper to analyze how self-reflective practices and self-study are part of ideological formations in teachers and how a systematic analysis of teacher’s ideology is the lens through which we are able to unpack and critically analyze the impact that ideology has on curriculum and instruction in classroom settings. The paper presents a methodology teacher can use to deconstruct and assess their ideology to improve their instruction and support students’ learning in the classroom. van Dijk (1998) multidisciplinary approach to the study of ideologies via discursive formation will be used as an analytical lens in this paper.