Sociodemographic and Clinical Characteristics Associated with Improvements in Quality of Life for Participants with Opioid Use Disorder

Background: The Houston Emergency Opioid Engagement System was established to create an access pathway into long-term recovery for individuals with opioid use disorder. The program determines effectiveness across multiple dimensions, one of which is by measuring the participant’s reported quality of life (QoL) at the beginning of the program and at successive intervals. Methods: A visual analog scale was used to measure the change in QoL among participants after joining the program. We then identified sociodemographic and clinical characteristics associated with changes in QoL. Results: 71% of the participants (n = 494) experienced an increase in their QoL scores, with an average improvement of 15.8 ± 29 points out of a hundred. We identified 10 factors associated with a significant change in QoL. Participants who relapsed during treatment experienced minor increases in QoL, and participants who attended professional counseling experienced the largest increases in QoL compared with those who did not. Conclusions: Insight into significant factors associated with increases in QoL may inform programs on areas of focus. The inclusion of counseling and other services that address factors such as psychological distress were found to increase participants’ QoL and success in recovery.

Dalfampridine for Mobility Limitations in People With Multiple Sclerosis May Be Augmented by Physical Therapy: A Non-randomized Two-Group Proof-of-Concept Pilot Study

Purpose: To demonstrate proof-of-concept for a combined physical therapy and pharmacological intervention and obtain preliminary estimates of the therapeutic efficacy of a motor-relearning physical therapy intervention with and without concurrent dalfampridine treatment on gait speed in people with mobility limitations due to multiple sclerosis (MS).Methods: Using a non-randomized, two-group design, 4 individuals with MS newly prescribed dalfampridine as part of their routine medical care, and 4 individuals with MS not taking dalfampridine completed a 3-week drug run-in or no-treatment baseline, respectively. After 3 weeks, all participants commenced physical therapy twice weekly for 6 weeks. Participants taking dalfampridine took the medication for the study duration. The physical therapy program comprised functional strengthening, gait training, balance training, and dual-task training. The primary outcome was Timed 25-foot Walk (T25FW) at the end of the 6-week physical therapy program.Results: For the 4 participants taking dalfampridine, average improvement in T25FW on drug only was 12.8% (95% CI 1.2 to 24.4%). During the 6-week physical therapy phase, both groups significantly improved T25FW, but the effect tended to favor the group taking dalfampridine (mean difference = −0.93 s, 95% CI −1.9 to 0.07 s, p = 0.064, d = 1.6). Whereas the physical therapy group had average T25FW improvement of 10.8% (95% CI 1.0 to 20.5%), the physical therapy plus dalfampridine group demonstrated average improvement of 20.7% (95% CI 3.8 to 37.6%).Conclusions: Further research is warranted to examine whether dalfampridine for mobility impairment may be augmented by physical therapy in people with MS.

MYOFASCIAL RELEASE AND SUPERFICIAL BACKLINE STRETCHING ON HAMSTRING STRAIN IN CRICKET PLAYERS: AN INTERVENTIONAL STUDY

Background: Hamstring Strain is common among athletes which lead to development of injury. Superficial Backline stretching for improving range of motion and flexibility. Using Tennis ball is a form of self-myofascial release results in increasing range of motion. Literature lacks studies done on self- myofascial release and superficial backline stretching. Hence my intention towards this study in comparison to find out the effect of Self Myofascial Release using tennis ball and superficial backline stretching on hamstring strain in cricket players. Methodology: A total of 24 subjects who were between the age group of 15 -19 years were conveniently allocated based on the inclusion criteria. Subjects received self-myofascial release using tennis ball 60 sec with 3-4 repetitions and 1 min interval of rest between sessions and superficial backline stretching with different poses for 2-3 repetitions and then compared FMS score of all subjects pre and post intervention after giving the superficial backline stretches and myofascial release to all the subjects. Outcome measure: Functional movement screen (FMS) Results: The result shows that there is a significant difference in pre and post Score of FMS, pre-FMS score is 15.9167±2.60295 and post score increased to 19.2500±1.59483which shows thatthere is statistical and clinical difference between the pre and post intervention. Functional movement is measured as the primary outcome measure.There is an average improvement of 3.333 with t value 12.487 and p <0.05. Conclusion: The aim of the study was to compare and find out the effect of Self Myofascial Release using tennis ball and superficial backline stretching on hamstring strain in cricket players., the result showed that there is statistically significant self-myofascial release using tennis ball and superficial backline stretching.

An Open-Label, Pilot Trial of HRG80™ Red Ginseng in Chronic Fatigue Syndrome, Fibromyalgia, and Post-Viral Fatigue

Chronic fatigue syndrome and fibromyalgia (CFS/FMS) affect 2.1% of the world’s population and ~10–25% of people who have had COVID-19. Previous clinical data suggested that a unique Panax ginseng (C.A. Meyer, family Araliaceae) root extract (HRG80™ Red Ginseng) often resulted in marked improvement. We aimed to study this hydroponic form of red ginseng root, containing high levels of rare ginsenosides, for improving energy, cognition, and stamina. This open-label prospective study included participants with severe CFS/FMS who took a daily supplement of HRG80 capsules (200–400 mg) or tablets (100–200 mg) for one month. A total of 188 subject patients completed the one-month treatment trial. Of these, 60.1% rated themselves as improved, with 13.3% rating themselves as being much better. In this group, the mean composite score improved from 11.9 to 18.8 (p < 0.001), with a 67% average increase in energy, 44% average increase in overall well-being, 48% average improvement in mental clarity, 58% average composite improvement in the previous three measurements (primary outcome measure), 46% average improvement in sleep, 33% average decrease in pain, and 72% average increase in stamina. Our study showed that HRG80 red ginseng root powder resulted in a marked improvement in people with CFS and fibromyalgia. This included the subgroup with post-viral CFS/FMS.

IMPROVING ENGLISH VOCABULARIES THROUGH DERIVATIONAL MORPHEME

This research aims to improve students' vocabulary acquisition through the strategy of deriving suffixes. The populations of this study are students from Politeknik Ganesha Medan in second semester. This research was conducted through Applied Classroom Action Research (CAR), born in two cycles (cycle 1 and cycle 2), and each process consisted of four meetings. The vocabulary test results showed that the average improvement score of the pre-test was 34.66, the post-test of period 1 was 93.46, and the post-test of period 2 was 97.33. In the first cycle, the nominal comparison rate was 88.83%, the verb nature was 77.46%, the adjective nature was 40.74%, and the adverbial heart was 84.14%. In cycle 2, the nominal comparison rate was 100%, the verb nature was 95.45%, the adjective nature was 65.84%, and the adverbial nature was 94.91%. The proportion of students who passed Minimal Criterion Mastery in each cycle was 100%, cycle 1 increased by 169.64%, and cycle 2 increased by 180.81%. These indicate that there is a significant improvement of the students’ vocabulary mastery through derivational morpheme strategy of the students of Politeknik Ganesha Medan.

Using Graph Representation in Host-Based Intrusion Detection

Cybersecurity has become an important part of our daily lives. As an important part, there are many researches on intrusion detection based on host system call in recent years. Compared to sentences, a sequence of system calls has unique characteristics. It contains implicit pattern relationships that are less sensitive to the order of occurrence and that have less impact on the classification results when the frequency of system calls varies slightly. There are also various properties such as resource consumption, execution time, predefined rules, and empirical weights of system calls. Commonly used word embedding methods, such as Bow, TI-IDF, N-Gram, and Word2Vec, do not fully exploit such relationships in sequences as well as conveniently support attribute expansion. To solve these problems, we introduce Graph Representation based Intrusion Detection (GRID), an intrusion detection framework based on graph representation learning. It captures the potential relationships between system calls to learn better features, and it is applicable to a wide range of back-end classifiers. GRID utilizes a new sequence embedding method Graph Random State Embedding (GRSE) that uses graph structures to model a finite number of sequence items and represent the structural association relationships between them. A more efficient representation of sequence embeddings is generated by random walks, word embeddings, and graph pooling. Moreover, it can be easily extended to sequences with attributes. Our experimental results on the AFDA-LD dataset show that GRID has an average improvement of 2% using the GRSE embedding method comparing to others.

High-Precision Potential Evapotranspiration Model Using GNSS Observation

Potential evapotranspiration (PET) can reflect the characteristics of drought change in different time scales and is the key parameter for calculating the standardized precipitation evapotranspiration index (SPEI). The Thornthwaite (TH) and Penman–Monteith (PM) models are generally used to calculate PET, but the precision of PET derived from the TH model is poor, and a large number of meteorological parameters are required to evaluate the PM model. To obtain high-precision PET with fewer meteorological parameters, a high-precision PET (HPET) model is proposed to calculate PET by introducing precipitable water vapor (PWV) from Global Navigation Satellite System (GNSS) observation. The PET difference (DPET) between TH- and PM-derived PET was calculated first. Then, the relationship between the DPET and GNSS-derived PWV/temperature was analysed, and a piecewise linear regression model was calculated to fit the DPET. Finally, the HPET model was established by adding the fitted DPET to the initial PET derived from the TH model. The Loess Plateau (LP) was selected as the experiment area, and the statistical results show the satisfactory performance of the proposed HPET model. The averaged root mean square (RMS) of the HPET model over the whole LP area is 8.00 mm, whereas the values for the TH and revised TH (RTH) models are 34.25 and 12.55 mm, respectively, when the PM-derived PET is regarded as the reference. Compared with the TH and RTH models, the average improvement rates of the HPET model over the whole LP area are 77.5 and 40.5%, respectively. In addition, the HPET-derived SPEI is better than that of the TH and RTH models at different month scales, with average improvement rates of 49.8 and 23.1%, respectively, over the whole LP area. Such results show the superiority of the proposed HPET model to the existing PET models.

Orthogonality-Constrained CNMF-Based Noise Reduction with Reduced Degradation of Biological Sound

The number of deaths due to cardiovascular and respiratory diseases is increasing annually. Cardiovascular diseases with high mortality rates, such as strokes, are frequently caused by atrial fibrillation without subjective symptoms. Chronic obstructive pulmonary disease is another condition in which early detection is difficult owing to the slow progression of the disease. Hence, a device that enables the early diagnosis of both diseases is necessary. In our previous study, a sensor for monitoring biological sounds such as vascular and respiratory sounds was developed and a noise reduction method based on semi-supervised convolutive non-negative matrix factorization (SCNMF) was proposed for the noisy environments of users. However, SCNMF attenuated part of the biological sound in addition to the noise. Therefore, this paper proposes a novel noise reduction method that achieves less distortion by imposing orthogonality constraints on the SCNMF. The effectiveness of the proposed method was verified experimentally using the biological sounds of 21 subjects. The experimental results showed an average improvement of 1.4 dB in the signal-to-noise ratio and 2.1 dB in the signal-to-distortion ratio over the conventional method. These results demonstrate the capability of the proposed approach to measure biological sounds even in noisy environments.

BERT_SE: A Pre-Trained Language Representation Model for Software Engineering

The application of Natural Language Processing (NLP) has achieved a high level of relevance in several areas. In the field of software engineering (SE), NLP applications are based on the classification of similar texts (e.g. software requirements), applied in tasks of estimating software effort, selection of human resources, etc. Classifying software requirements has been a complex task, considering the informality and complexity inherent in the texts produced during the software development process. The pre-trained embedding models are shown as a viable alternative when considering the low volume of textual data labeled in the area of software engineering, as well as the lack of quality of these data. Although there is much research around the application of word embedding in several areas, to date, there is no knowledge of studies that have explored its application in the creation of a specific model for the domain of the SE area. Thus, this article presents the proposal for a contextualized embedding model, called BERT_SE, which allows the recognition of specific and relevant terms in the context of SE. The assessment of BERT_SE was performed using the software requirements classification task, demonstrating that this model has an average improvement rate of 13% concerning the BERT_base model, made available by the authors of BERT. The code and pre-trained models are available at https://github.com/elianedb.

DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation

Carcinogenicity testing plays an essential role in identifying carcinogens in environmental chemistry and drug development. However, it is a time-consuming and label-intensive process to evaluate the carcinogenic potency with conventional 2-years rodent animal studies. Thus, there is an urgent need for alternative approaches to providing reliable and robust assessments on carcinogenicity. In this study, we proposed a DeepCarc model to predict carcinogenicity for small molecules using deep learning-based model-level representations. The DeepCarc Model was developed using a data set of 692 compounds and evaluated on a test set containing 171 compounds in the National Center for Toxicological Research liver cancer database (NCTRlcdb). As a result, the proposed DeepCarc model yielded a Matthews correlation coefficient (MCC) of 0.432 for the test set, outperforming four advanced deep learning (DL) powered quantitative structure-activity relationship (QSAR) models with an average improvement rate of 37%. Furthermore, the DeepCarc model was also employed to screen the carcinogenicity potential of the compounds from both DrugBank and Tox21. Altogether, the proposed DeepCarc model could serve as an early detection tool (https://github.com/TingLi2016/DeepCarc) for carcinogenicity assessment.